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MLS-C01 AWS Certified Machine Learning - Specialty Question and Answers

Question # 4

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

A.

A naive Bayesian model, since the features are all conditionally independent.

B.

A full Bayesian network, since the features are all conditionally independent.

C.

A naive Bayesian model, since some of the features are statistically dependent.

D.

A full Bayesian network, since some of the features are statistically dependent.

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Question # 5

A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials.

...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.

...should the company retrain the model to meet these requirements?

A.

Set the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

B.

Set the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.

C.

Use 90% of the historical data for training Set the number of epochs to 20.

D.

Set the normalize_jabel hyperparameter to true. Set the number of classes to 2.

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Question # 6

A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

A.

Use an Amazon SageMaker notebook for both feature engineering and model development

B.

Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

C.

Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development

D.

Use Amazon ML for both feature engineering and model development.

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Question # 7

A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs

What does the Specialist need to do1?

A.

Bundle the NVIDIA drivers with the Docker image

B.

Build the Docker container to be NVIDIA-Docker compatible

C.

Organize the Docker container's file structure to execute on GPU instances.

D.

Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

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Question # 8

A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that

resource utilization is not optimal.

What should the data scientist do to identify and address training issues with the LEAST development effort?

A.

Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.

B.

Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.

C.

Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.

D.

Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.

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Question # 9

Acybersecurity company is collecting on-premises server logs, mobile app logs, and loT sensor data. The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis. Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances. The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow.

Which solution will meet these requirements MOST cost-effectively?

A.

Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Configure the data sources to send data to the delivery streams.

B.

Create one Amazon Kinesis data stream. Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Connect the delivery streams to the data stream. Configure the data sources to send data to the data stream.

C.

Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the raw data to the S3 bucket. Configure the data sources to send data to the delivery stream.

D.

Create one Amazon Kinesis data stream. Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the data to the S3 bucket. Connect the delivery stream to the data stream. Configure the data sources to send data to the data stream.

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Question # 10

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.

The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes

What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

A.

Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.

B.

Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

C.

Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.

D.

Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

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Question # 11

A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation.

Which solution will meet these requirements with the LEAST development effort?

A.

Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.

B.

Train a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.

C.

Train an Amazon SageMaker BlazingText model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.

D.

Index company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.

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Question # 12

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.

What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

A.

Root Mean Square Error (RMSE)

B.

Residual plots

C.

Area under the curve

D.

Confusion matrix

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Question # 13

A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.

The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.

Which action will MOST likely improve the performance for the forecasting model?

A.

Aggregate sales from stores in the same geographic area.

B.

Apply smoothing to correct for seasonal variation.

C.

Change the forecast frequency from daily to weekly.

D.

Replace missing values in the dataset by using linear interpolation.

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Question # 14

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A.

The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%

B.

The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%

C.

The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).

D.

The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

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Question # 15

IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning

The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:

• Solution simplicity

• Fast development time

• Low cost

• High flexibility

What technologies meet the company's requirements?

A.

Amazon S3 and Amazon Athena

B.

Amazon Redshift and AWS Glue

C.

Amazon DynamoDB and DynamoDB Accelerator (DAX)

D.

Amazon RDS and Amazon ES

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Question # 16

A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora

• Profiles for all past and existing customers

• Profiles for all past and existing insured pets

• Policy-level information

• Premiums received

• Claims paid

What steps should be taken to implement a machine learning model to identify potential new customers on social media?

A.

Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

B.

Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

C.

Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

D.

Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

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Question # 17

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant

will default on a credit card payment. The company has collected data from a large number of sources with

thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are

highly correlated, the large number of features slows down the training speed significantly, and that there are

some overfitting issues.

The Data Scientist on this project would like to speed up the model training time without losing a lot of

information from the original dataset.

Which feature engineering technique should the Data Scientist use to meet the objectives?

A.

Run self-correlation on all features and remove highly correlated features

B.

Normalize all numerical values to be between 0 and 1

C.

Use an autoencoder or principal component analysis (PCA) to replace original features with new features

D.

Cluster raw data using k-means and use sample data from each cluster to build a new dataset

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Question # 18

A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city as this is a prototype, only daily data from the last year is available

Which model is MOST likely to provide the best results in Amazon SageMaker?

A.

Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting ofthe full year of data with a predictor_type of regressor.

B.

Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year ofdata.

C.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of regressor.

D.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of classifier.

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Question # 19

A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.

Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

A.

Specificity

B.

False positive rate

C.

Accuracy

D.

Fl score

E.

True positive rate

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Question # 20

A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

A.

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

B.

Use AWS Glue to catalogue the data and Amazon Athena to run queries

C.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes

D.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

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Question # 21

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.

What should the Specialist do to meet this objective?

A.

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

B.

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

C.

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

D.

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

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Question # 22

A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10.000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels * 224 pixels. After several training runs, the model is overfitting on the training data.

Which actions should the ML specialist take to address this problem? (Select TWO.)

A.

Use Amazon SageMaker Ground Truth to label the unlabeled images

B.

Use image preprocessing to transform the images into grayscale images.

C.

Use data augmentation to rotate and translate the labeled images.

D.

Replace the activation of the last layer with a sigmoid.

E.

Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.

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Question # 23

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

A.

The class distribution in the dataset is imbalanced

B.

Dataset shuffling is disabled

C.

The batch size is too big

D.

The learning rate is very high

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Question # 24

A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.

What is the MOST effective way to encode this categorical feature into a numeric feature?

A.

Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

B.

Fix the spelling in the column by using char-RNN. Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.

C.

Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.

D.

Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.

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Question # 25

A company wants to predict the classification of documents that are created from an application. New documents are saved to an Amazon S3 bucket every 3 seconds. The company has developed three versions of a machine learning (ML) model within Amazon SageMaker to classify document text. The company wants to deploy these three versions to predict the classification of each document.

Which approach will meet these requirements with the LEAST operational overhead?

A.

Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document.

B.

Deploy all the models to a single SageMaker endpoint. Treat each model as a production variant. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each production variant and return the results of each model.

C.

Deploy each model to its own SageMaker endpoint Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each endpoint and return the results of each model.

D.

Deploy each model to its own SageMaker endpoint. Create three AWS Lambda functions. Configure each Lambda function to call a different endpoint and return the results. Configure three S3 event notifications to invoke the Lambda functions when new documents are created.

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Question # 26

A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions

What metric is BEST suited to score the model?

A.

Precision

B.

Recall

C.

Area Under the ROC Curve (AUC)

D.

Root Mean Square Error (RMSE)

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Question # 27

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical

features. The Marketing team has not provided any insight about which features are relevant for churn

prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on

the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide

gap between the training and validation set accuracy.

Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s

needs? (Choose two.)

A.

Add L1 regularization to the classifier

B.

Add features to the dataset

C.

Perform recursive feature elimination

D.

Perform t-distributed stochastic neighbor embedding (t-SNE)

E.

Perform linear discriminant analysis

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Question # 28

A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.

The data scientist wants to understand the variance in the data along various directions in the feature space.

Which solution will meet these requirements?

A.

Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.

B.

Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.

C.

Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.

D.

Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.

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Question # 29

A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.

After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.

Which solutions will meet this requirement? (Select TWO.)

A.

Increase the number of passes on the existing training data. Perform more hyperparameter tuning.

B.

Increase the amount of regularization. Use fewer feature combinations.

C.

Add new domain-specific features. Use more complex models.

D.

Use fewer feature combinations. Decrease the number of numeric attribute bins.

E.

Decrease the amount of training data examples. Reduce the number of passes on the existing training data.

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Question # 30

A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations.

Which solution will meet these requirements with LEAST development effort?

A.

Use AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

B.

Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.

C.

Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

D.

Use Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.

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Question # 31

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A.

Redeploy the model as a batch transform job on an M5 instance.

B.

Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C.

Redeploy the model on a P3dn instance.

D.

Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

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Question # 32

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an,” and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.

What should the data scientist do to meet these requirements?

A.

Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.

B.

Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.

C.

Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.

D.

Remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.

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Question # 33

Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

A.

Pre-split the data before uploading to Amazon S3

B.

Have Amazon ML split the data randomly.

C.

Have Amazon ML split the data sequentially.

D.

Perform custom cross-validation on the data

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Question # 34

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.

How can the ML team solve this issue?

A.

Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.

B.

Replace the current endpoint with a multi-model endpoint using SageMaker.

C.

Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.

D.

Increase the cooldown period for the scale-out activity.

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Question # 35

A company wants to conduct targeted marketing to sell solar panels to homeowners. The company wants to use machine learning (ML) technologies to identify which houses already have solar panels. The company has collected 8,000 satellite images as training data and will use Amazon SageMaker Ground Truth to label the data.

The company has a small internal team that is working on the project. The internal team has no ML expertise and no ML experience.

Which solution will meet these requirements with the LEAST amount of effort from the internal team?

A.

Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.

B.

Set up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.

C.

Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.

D.

Set up a public workforce. Use the public workforce to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.

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Question # 36

A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.

The data scientists are using Amazon Forecast to generate the forecasts.

Which algorithm in Forecast should the data scientists use to meet these requirements?

A.

Autoregressive Integrated Moving Average (AIRMA)

B.

Exponential Smoothing (ETS)

C.

Convolutional Neural Network - Quantile Regression (CNN-QR)

D.

Prophet

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Question # 37

A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU: GPU ratio of 12:1 to train the models.

The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time The ML specialist must reduce training costs without increasing the duration of the training jobs.

Which solution will meet these requirements?

A.

Switch to an instance type that has only CPUs.

B.

Use a heterogeneous cluster that has two different instances groups.

C.

Use memory-optimized EC2 Spot Instances for the training jobs.

D.

Switch to an instance type that has a CPU GPU ratio of 6:1.

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Question # 38

A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.

Which visualization will help the data scientist better understand the data trend?

A.

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.

B.

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.

C.

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.

D.

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.

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Question # 39

A business to business (B2B) ecommerce company wants to develop a fair and equitable risk mitigation strategy to reject potentially fraudulent transactions. The company wants to reject fraudulent transactions despite the possibility of losing some profitable transactions or customers.

Which solution will meet these requirements with the LEAST operational effort?

A.

Use Amazon SageMaker to approve transactions only for products the company has sold in the past.

B.

Use Amazon SageMaker to train a custom fraud detection model based on customer data.

C.

Use the Amazon Fraud Detector prediction API to approve or deny any activities that Fraud Detector identifies as fraudulent.

D.

Use the Amazon Fraud Detector prediction API to identify potentially fraudulent activities so the company can review the activities and reject fraudulent transactions.

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Question # 40

A Machine Learning Specialist works for a credit card processing company and needs to predict which

transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the

probability that a given transaction may fraudulent.

How should the Specialist frame this business problem?

A.

Streaming classification

B.

Binary classification

C.

Multi-category classification

D.

Regression classification

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Question # 41

A data scientist receives a new dataset in .csv format and stores the dataset in Amazon S3. The data scientist will use this dataset to train a machine learning (ML) model.

The data scientist first needs to identify any potential data quality issues in the dataset. The data scientist must identify values that are missing or values that are not valid. The data scientist must also identify the number of outliers in the dataset.

Which solution will meet these requirements with the LEAST operational effort?)

A.

Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.

B.

Leave the dataset in .csv format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.

C.

Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

D.

Leave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

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Question # 42

A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will

have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot

Instances in the EMR cluster.

Which nodes should the Specialist launch on Spot Instances?

A.

Master node

B.

Any of the core nodes

C.

Any of the task nodes

D.

Both core and task nodes

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Question # 43

A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.

Which approach allows the Specialist to use all the data to train the model?

A.

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the trainingcode is executing and the model parameters seem reasonable. Initiate a SageMaker training job using thefull dataset from the S3 bucket using Pipe input mode.

B.

Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstance. Train on a small amount of the data to verify the training code and hyperparameters. Go back toAmazon SageMaker and train using the full dataset

C.

Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatiblewith Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket usingPipe input mode.

D.

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the trainingcode is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with anAWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

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Question # 44

A company is building a predictive maintenance system using real-time data from devices on remote sites. There is no AWS Direct Connect connection or VPN connection between the sites and the company’s VPC. The data needs to be ingested in real time from the devices into Amazon S3.

Transformation is needed to convert the raw data into clean .csv data to be fed into the machine learning (ML) model. The transformation needs to happen during the ingestion process. When transformation fails, the records need to be stored in a specific location in Amazon S3 for human review. The raw data before transformation also needs to be stored in Amazon S3.

How should an ML specialist architect the solution to meet these requirements with the LEAST effort?

A.

Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an AWS Lambda function for data transformation. Enable source record backup on Firehose.

B.

Use Amazon Managed Streaming for Apache Kafka. Set up workers in Amazon Elastic Container Service (Amazon ECS) to move data from Kafka brokers to Amazon S3 while transforming it. Configure workers to store raw and unsuccessfully transformed data in different S3 buckets.

C.

Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an Apache Spark job in AWS Glue for data transformation. Enable source record backup and configure the error prefix.

D.

Use Amazon Kinesis Data Streams in front of Amazon Data Firehose. Use Kinesis Data Streams with AWS Lambda to store raw data in Amazon S3. Configure Firehose to invoke a Lambda function for data transformation with Amazon S3 as the destination.

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Question # 45

A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users’ interactions on the platform and from sources such as external websites and social media.

The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.

Which approach will address all of these requirements with the LEAST development effort?

A.

Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.

B.

Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.

C.

Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.

D.

Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.

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Question # 46

A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model’s accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.

Which solution provides the MOST accurate result?

A.

Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this HPO job.

B.

Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.

C.

Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this training job.

D.

Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.

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Question # 47

A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.

What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

A.

1 shards

B.

10 shards

C.

100 shards

D.

1,000 shards

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Question # 48

A data science team is working with a tabular dataset that the team stores in Amazon S3. The team wants to experiment with different feature transformations such as categorical feature encoding. Then the team wants to visualize the resulting distribution of the dataset. After the team finds an appropriate set of feature transformations, the team wants to automate the workflow for feature transformations.

Which solution will meet these requirements with the MOST operational efficiency?

A.

Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Use SageMaker Data Wrangler templates for visualization. Export the feature processing workflow to a SageMaker pipeline for automation.

B.

Use an Amazon SageMaker notebook instance to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.

C.

Use AWS Glue Studio with custom code to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.

D.

Use Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualzation. Package each feature transformation step into a separate AWS Lambda function. Use AWS Step Functions for workflow automation.

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Question # 49

A real estate company wants to create a machine learning model for predicting housing prices based on a

historical dataset. The dataset contains 32 features.

Which model will meet the business requirement?

A.

Logistic regression

B.

Linear regression

C.

K-means

D.

Principal component analysis (PCA)

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Question # 50

A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.

The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data …. ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpoint

Which combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

A.

Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.

B.

Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,

C.

Include an inline policy for the data scientist’s 1AM user that allows SageMaker to read S3 objects

D.

Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.

E.

Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query.

F.

Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.

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Question # 51

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

A.

Linear regression is inappropriate. The residuals do not have constant variance.

B.

Linear regression is inappropriate. The underlying data has outliers.

C.

Linear regression is appropriate. The residuals have a zero mean.

D.

Linear regression is appropriate. The residuals have constant variance.

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Question # 52

A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.

.... Dh Amazon SageMaker algorithm will meet this requirement?

A.

XGBoost

B.

Image Classification - TensorFlow

C.

Object Detection - TensorFlow

D.

Semantic segmentation - MXNet

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Question # 53

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models

During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images

Which of the following should be used to resolve this issue? (Select TWO)

A.

Add vanishing gradient to the model

B.

Perform data augmentation on the training data

C.

Make the neural network architecture complex.

D.

Use gradient checking in the model

E.

Add L2 regularization to the model

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Question # 54

A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

Which modeling approach will deliver the MOST accurate prediction of product quality?

A.

Amazon SageMaker DeepAR forecasting algorithm

B.

Amazon SageMaker XGBoost algorithm

C.

Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm

D.

A convolutional neural network (CNN) and ResNet

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Question # 55

A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.

Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes.

Which solution will meet these requirements?

A.

Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe.

B.

Instead Of File mode, configure the SageMaker training job to use FastFile mode with no Other changes.

C.

Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no Other changes.

D.

Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Implement model checkpoints.

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Question # 56

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.

The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company’s business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.

Which solution satisfies these requirements with MINIMAL effort?

A.

Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.

B.

Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.

C.

Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.

D.

Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.

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Question # 57

A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only

How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A.

Convert the records to Apache Parquet format

B.

Convert the records to JSON format

C.

Convert the records to GZIP CSV format

D.

Convert the records to XML format

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Question # 58

A retail company uses a machine learning (ML) model for daily sales forecasting. The company’s brand manager reports that the model has provided inaccurate results for the past 3 weeks.

At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company’s ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.

What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

A.

Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.

B.

Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.

C.

Create a line chart with the weekly mean absolute error (MAE) of the model.

D.

Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.

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Question # 59

A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.

Which solution should the Specialist recommend?

A.

Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

B.

A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database

C.

Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

D.

Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database

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Question # 60

A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy

sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as

either a potential risk or no risk. The model is not performing well, even though the Data Scientist has

experimented with many different network structures and tuned the corresponding hyperparameters.

Which approach will provide the MAXIMUM performance boost?

A.

Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a largecollection of news articles related to the energy sector.

B.

Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation lossstops decreasing.

C.

Reduce the learning rate and run the training process until the training loss stops decreasing.

D.

Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to theenergy sector.

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Question # 61

A company is using a machine learning (ML) model to recommend products to customers. An ML specialist wants to analyze the data for the most popular recommendations in four dimensions.

The ML specialist will visualize the first two dimensions as coordinates. The third dimension will be visualized as color. The ML specialist will use size to represent the fourth dimension in the visualization.

Which solution will meet these requirements?

A.

Use the Amazon SageMaker Data Wrangler bar chart feature. Use Group By to represent the third and fourth dimensions.

B.

Use the Amazon SageMaker Canvas box plot visualization. Use color and fill pattern to represent the third and fourth dimensions.

C.

Use the Amazon SageMaker Data Wrangler histogram feature. Use color and fill pattern to represent the third and fourth dimensions.

D.

Use the Amazon SageMaker Canvas scatter plot visualization. Use scatter point size and color to represent the third and fourth dimensions.

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Question # 62

A social media company wants to develop a machine learning (ML) model to detect Inappropriate or offensive content in images. The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model. The company also intends to use SageMaker pipe mode to speed up the training.

...company splits the dataset into training, validation, and testing datasets. The company stores the training and validation images in folders that are named Training and Validation, respectively. The folder ...ain subfolders that correspond to the names of the dataset classes. The company resizes the images to the same sue and generates two input manifest files named training.1st and validation.1st, for the ..ing dataset and the validation dataset. respectively. Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset.

...h additional data preparation steps should the company take before uploading the files to Amazon S3?

A.

Generate two Apache Parquet files, training.parquet and validation.parquet. by reading the images into a Pandas data frame and storing the data frame as a Parquet file. Upload the Parquet files to the training S3 bucket

B.

Compress the training and validation directories by using the Snappy compression library Upload the manifest and compressed files to the training S3 bucket

C.

Compress the training and validation directories by using the gzip compression library. Upload the manifest and compressed files to the training S3 bucket.

D.

Generate two RecordIO files, training rec and validation.rec. from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordlO files to the training S3 bucket.

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Question # 63

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.

What should the Specialist do to initialize the model to re-train it with the custom data?

A.

Initialize the model with random weights in all layers including the last fully connected layer

B.

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

C.

Initialize the model with random weights in all layers and replace the last fully connected layer

D.

Initialize the model with pre-trained weights in all layers including the last fully connected layer

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Question # 64

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.

Which steps would improve the accuracy of the solution? (Choose three.)

A.

Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.

B.

Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.

C.

Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.

D.

Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

E.

Use Amazon Rekognition Custom Labels to train a new model.

F.

Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

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Question # 65

A company wants to predict the sale prices of houses based on available historical sales data. The target

variable in the company’s dataset is the sale price. The features include parameters such as the lot size, living

area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built,

and postal code. The company wants to use multi-variable linear regression to predict house sale prices.

Which step should a machine learning specialist take to remove features that are irrelevant for the analysis

and reduce the model’s complexity?

A.

Plot a histogram of the features and compute their standard deviation. Remove features with high variance.

B.

Plot a histogram of the features and compute their standard deviation. Remove features with low variance.

C.

Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.

D.

Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

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Question # 66

A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 ТВ of training data that consists of labeled images of defective product parts. The training data is in the corporate on-premises data center.

The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company’s use of an ML model in the low-connectivity environments.

Which solution will meet these requirements?

A.

Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.

B.

Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.

C.

Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

D.

Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

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Question # 67

A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

A.

1

B.

4

C.

7

D.

10

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Question # 68

A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.

A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.

What is the MOST direct approach to solve this problem within 2 days?

A.

Train a custom classifier by using Amazon Comprehend.

B.

Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.

C.

Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.

D.

Use a built-in seq2seq model in Amazon SageMaker.

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Question # 69

The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

A.

The model predicts both the trend and the seasonality well.

B.

The model predicts the trend well, but not the seasonality.

C.

The model predicts the seasonality well, but not the trend.

D.

The model does not predict the trend or the seasonality well.

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Question # 70

A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.

Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

A.

Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.

B.

Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.

C.

Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.

D.

Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

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Question # 71

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.

What changes should the Specialist consider to solve this issue? (Choose three.)

A.

Choose a higher number of layers

B.

Choose a lower number of layers

C.

Choose a smaller learning rate

D.

Enable dropout

E.

Include all the images from the test set in the training set

F.

Enable early stopping

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Question # 72

A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago

Which method should the Specialist try to improve model performance?

A.

The model needs to be completely re-engineered because it is unable to handle product inventory changes

B.

The model's hyperparameters should be periodically updated to prevent drift

C.

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

D.

The model should be periodically retrained using the original training data plus new data as product inventory changes

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Question # 73

A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not

Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

A.

Seq2seq

B.

XGBoost

C.

K-means

D.

Random Cut Forest (RCF)

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Question # 74

A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.

The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.

Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

A.

Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.

B.

Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.

C.

Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.

D.

Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page

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Question # 75

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.

How should the company prepare the data for the model to improve the model's accuracy?

A.

Adjust the class weight to account for each machine type.

B.

Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).

C.

Undersample the non-failure events. Stratify the non-failure events by machine type.

D.

Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

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Question # 76

A company operates large cranes at a busy port. The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.

The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and …perature for each crane. The company contracts AWS ML experts to implement an ML solution.

Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Select TWO.)

A.

The historical sensor data does not include a significant number of data points and attributes for certain time periods.

B.

The historical sensor data shows that simple rule-based thresholds can predict crane failures.

C.

The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.

D.

The historical sensor data from the cranes are available with high granularity for the last 3 years.

E.

The historical sensor data contains most common types of crane failures that the company wants to predict.

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Question # 77

A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain.

The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP). The application will then route users to the appropriate SageMaker Studio domain.

The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department.

How should the company meet these requirements?

A.

Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.

B.

Use the SageMaker CreateHuman TaskUi API to generate a UI URL. Pass the URL to the proxy application.

C.

Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL.

D.

Use the SageMaker CreatePresignedNotebookInstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.

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Question # 78

A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.

Which is the FASTEST route to index the assets?

A.

Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.

B.

Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.

C.

Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.

D.

Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.

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Question # 79

A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data.

The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the data. The data scientists also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data.

Which solution will meet these requirements with the LEAST amount of compute resources?

A.

Import the data by using the None option.

B.

Import the data by using the Stratified option.

C.

Import the data by using the First K option. Infer the value of K from domain knowledge.

D.

Import the data by using the Randomized option. Infer the random size from domain knowledge.

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Question # 80

A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions -

Here is an example from the dataset

"The quck BROWN FOX jumps over the lazy dog "

Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

A.

Perform part-of-speech tagging and keep the action verb and the nouns only

B.

Normalize all words by making the sentence lowercase

C.

Remove stop words using an English stopword dictionary.

D.

Correct the typography on "quck" to "quick."

E.

One-hot encode all words in the sentence

F.

Tokenize the sentence into words.

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Question # 81

A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.

The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.

How should the developer verify the suitability of an ARIMA approach?

A.

Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.

B.

Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

C.

Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.

D.

Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

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Question # 82

A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.

Which implementation will meet these requirements?

A.

Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

B.

Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.

C.

Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

D.

Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.

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Question # 83

A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.

Which solution will meet these requirements with the LEAST development effort?

A.

Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

B.

Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.

C.

Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

D.

Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

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Question # 84

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant

Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

A.

Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

B.

Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

C.

Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

D.

Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

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Question # 85

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

A.

m5 4xlarge (general purpose)

B.

r5.2xlarge (memory optimized)

C.

p3.2xlarge (GPU accelerated computing)

D.

p3 8xlarge (GPU accelerated computing)

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Question # 86

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:

March 1st, 2020, 08:14pm -

There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.

Which solution requires the LEAST operational overhead to create a new dataset with the added features?

A.

Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

B.

Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

C.

Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.

D.

Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

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Question # 87

A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.

Which cross-validation strategy should the Data Scientist adopt?

A.

A k-fold cross-validation strategy with k=5

B.

A stratified k-fold cross-validation strategy with k=5

C.

A k-fold cross-validation strategy with k=5 and 3 repeats

D.

An 80/20 stratified split between training and validation

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Question # 88

A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.

Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

A.

Precision = 0.91 Recall = 0.6

B.

Precision = 0.61 Recall = 0.98

C.

Precision = 0.7 Recall = 0.9

D.

Precision = 0.98 Recall = 0.8

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Question # 89

A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents?

A.

Convert current documents to SSML with pronunciation tags

B.

Create an appropriate pronunciation lexicon.

C.

Output speech marks to guide in pronunciation

D.

Use Amazon Lex to preprocess the text files for pronunciation

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Question # 90

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

A.

Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

B.

Logistic regression

C.

Support vector machine (SVM) with non-linear kernel

D.

Single perceptron with tanh activation function

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Question # 91

A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.

Which storage scheme is MOST adapted to this scenario?

A.

Store datasets as files in Amazon S3.

B.

Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.

C.

Store datasets as tables in a multi-node Amazon Redshift cluster.

D.

Store datasets as global tables in Amazon DynamoDB.

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Question # 92

A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

A.

Increase the max_depth parameter value.

B.

Lower the max_depth parameter value.

C.

Update the objective to binary:logistic.

D.

Lower the min_child_weight parameter value.

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Question # 93

A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions.

Which solution will meet these requirements?

A.

Amazon S3 with S3 Cross-Region Replication (CRR)

B.

Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region

C.

Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability

D.

AWS Storage Gateway Volume Gateway

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Question # 94

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data

Which type of data repository is the MOST cost-effective solution?

A.

An Amazon EBS-backed Amazon EC2 instance with hourly directories

B.

An Amazon RDS database with hourly table partitions

C.

An Amazon S3 data lake with hourly object prefixes

D.

An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

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Question # 95

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.

Which solution should the agency consider?

A.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and createa stream processor to detect faces from a collection of known employees, and alert when non-employeesare detected.

B.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detectfaces from a collection of known employees and alert when non-employees are detected.

C.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video toAmazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video andcreate a stream processor to detect faces from a collection on each stream, and alert when nonemployeesare detected.

D.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video toAmazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function tocapture image fragments and then call Amazon Rekognition Image to detect faces from a collection ofknown employees, and alert when non-employees are detected.

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Question # 96

A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.

What type of machine learning model should be used?

A.

Classification month-to-month using supervised learning of the 200 categories based on claim contents.

B.

Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

C.

Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

D.

Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

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Question # 97

A company operates an amusement park. The company wants to collect, monitor, and store real-time traffic data at several park entrances by using strategically placed cameras. The company's security team must be able to immediately access the data for viewing. Stored data must be indexed and must be accessible to the company's data science team.

Which solution will meet these requirements MOST cost-effectively?

A.

Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in integration with Amazon Rekognition for viewing by the security team.

B.

Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.

C.

Use Amazon Rekognition Video and the GStreamer plugin to ingest the data for viewing by the security team. Use Amazon Kinesis Data Streams to index and store the data.

D.

Use Amazon Data Firehose to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.

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Question # 98

A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.

An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job.

Which completion criteria will meet this requirement?

A.

MaxRuntimelnSeconds

B.

TargetObjectiveMetricValue

C.

CompleteOnConvergence

D.

MaxNumberOfTrainingJobsNotlmproving

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Question # 99

While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

A.

The model might have prediction errors over a range of target values.

B.

The dataset cannot be accurately represented using the regression model

C.

There are too many variables in the model

D.

The model is predicting its target values perfectly.

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