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DAS-C01 AWS Certified Data Analytics - Specialty Question and Answers

Question # 4

A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner.

Which solution meets these requirements?

A.

Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data.

B.

Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

C.

Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

D.

Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY command. Use Amazon Redshift Spectrum for less frequently queried data.

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Question # 5

A company’s data analyst needs to ensure that queries executed in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes. Queries that exceed the prescribed threshold must be canceled immediately.

What should the data analyst do to achieve this?

A.

Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.

B.

For each workgroup, set the control limit for each query to the prescribed threshold.

C.

Enforce the prescribed threshold on all Amazon S3 bucket policies

D.

For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.

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Question # 6

A company uses Amazon Redshift for its data warehouse. The company is running an ET L process that receives data in data parts from five third-party providers. The data parts contain independent records that are related to one specific job. The company receives the data parts at various times throughout each day.

A data analytics specialist must implement a solution that loads the data into Amazon Redshift only after the company receives all five data parts.

Which solution will meet these requirements?

A.

Create an Amazon S3 bucket to receive the data. Use S3 multipart upload to collect the data from the different sources andto form a single object before loading the data into Amazon Redshift.

B.

Use an AWS Lambda function that is scheduled by cron to load the data into a temporary table in Amazon Redshift. Use Amazon Redshift database triggers to consolidate the final data when all five data parts are ready.

C.

Create an Amazon S3 bucket to receive the data. Create an AWS Lambda function that is invoked by S3 upload events. Configure the function to validate that all five data parts are gathered before the function loads the data into Amazon Redshift.

D.

Create an Amazon Kinesis Data Firehose delivery stream. Program a Python condition that will invoke a buffer flush when all five data parts are received.

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Question # 7

A manufacturing company is storing data from its operational systems in Amazon S3. The company's business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athena. The company needs to access the Athena service from the on-premises network by using a JDBC connection. The company has created a VPC. Security policies mandate that requests to AWS services cannot traverse the internet.

Which combination of steps should a data analytics specialist take to meet these requirements? (Select TWO.)

A.

Establish an AWS Direct Connect connection between the on-premises network and the VPC.

B.

Configure the JDBC connection to connect to Athena through Amazon API Gateway.

C.

Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3.

D.

Configure the JDBC connection to use an interface VPC endpoint for Athena.

E.

Deploy Athena within a private subnet.

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Question # 8

A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.

Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners’ dashboards?

A.

Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.

B.

Create and schedule an AWS Glue Spark job to run every 5 minutes. The job inserts reference data into Amazon Redshift.

C.

Send reference data to Amazon Kinesis Data Streams. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.

D.

Send the reference data to an Amazon Kinesis Data Firehose delivery stream. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

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Question # 9

A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist isdefining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.

Which elements must each query monitoring rule include?

A.

A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours

B.

A queue name, a unique rule name, and a predicate-based stop condition

C.

A unique rule name, one to three predicates, and an action

D.

A workload name, a unique rule name, and a query runtime-based condition

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Question # 10

A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream.

After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredIteratorExceptions error sporadically.

What should the data analyst do to resolve this?

A.

Increase the number of threads that process the stream records.

B.

Increase the provisioned read capacity units assigned to the stream’s Amazon DynamoDB table.

C.

Increase the provisioned write capacity units assigned to the stream’s Amazon DynamoDB table.

D.

Decrease the provisioned write capacity units assigned to the stream’s Amazon DynamoDB table.

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Question # 11

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.

A trips fact table for information on completed rides. A drivers dimension table for driver profiles.

A customers fact table holding customer profile information.

The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.

What table design provides optimal query performance?

A.

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.

B.

Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.

C.

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.

D.

Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.

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Question # 12

A company wants to provide its data analysts with uninterrupted access to the data in its Amazon Redshift cluster. All data is streamed to an Amazon S3 bucket with Amazon Kinesis Data Firehose. An AWS Glue job that is scheduled to run every 5 minutes issues a COPY command to move the data into Amazon Redshift.

The amount of data delivered is uneven throughout the day, and cluster utilization is high during certain periods. The COPY command usually completes within a couple of seconds. However, when load spike occurs, locks can exist and data can be missed. Currently, the AWS Glue job is configured to run without retries, with timeout at 5 minutes and concurrency at 1.

How should a data analytics specialist configure the AWS Glue job to optimize fault tolerance and improve data availability in the Amazon Redshift cluster?

A.

Increase the number of retries. Decrease the timeout value. Increase the job concurrency.

B.

Keep the number of retries at 0. Decrease the timeout value. Increase the job concurrency.

C.

Keep the number of retries at 0. Decrease the timeout value. Keep the job concurrency at 1.

D.

Keep the number of retries at 0. Increase the timeout value. Keep the job concurrency at 1.

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Question # 13

A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, orapplications running in the same AWS account to comply with internal security policies.

Which solution meets these requirements?

A.

Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.

B.

Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.

C.

Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.

D.

Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

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Question # 14

An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.

Which steps will create the required logs?

A.

Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.

B.

Allow access to the Amazon Redshift database using AWS IAM only. Log access using AWS CloudTrail.

C.

Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.

D.

Enable and download audit reports from AWS Artifact.

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Question # 15

A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.

Which approach can provide the visuals that senior leadership requested with the least amount of effort?

A.

Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.

B.

Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.

C.

Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.

D.

Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.

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Question # 16

A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster's capacity to support the change in analytical and storage requirements.

Which solution meets these requirements?

A.

Resize the cluster using elastic resize with dense compute nodes.

B.

Resize the cluster using classic resize with dense compute nodes.

C.

Resize the cluster using elastic resize with dense storage nodes.

D.

Resize the cluster using classic resize with dense storage nodes.

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Question # 17

A company collects and transforms data files from third-party providers by using an on-premises SFTP server. The company uses a Pythonscript to transform the data.

The company wants to reduce the overhead of maintaining the SFTP server and storing large amounts of data on premises. However, the company does not want to change the existing upload process for the third-party providers.

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

A.

Deploy the Python script on an Amazon EC2 instance. Install a third-party SFTP server on the EC2 instance. Schedule the script to run periodically on the EC2 instance to perform a data transformation on new files. Copy the transformed files to Amazon S3.

B.

Create an Amazon S3 bucket that includes a separate prefix for each provider. Provide the S3 URL to each provider for its respective prefix. Instruct the providers to use the S3 COPY command to upload data. Configure an AWS Lambda function that transforms the data when new files are uploaded.

C.

Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Schedule a Python shell job in AWS Glue to use the existing Python script to run periodically and transform the uploaded files.

D.

Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Use AWS Data Pipeline to schedule a transient Amazon EMR cluster with an Apache Spark step to periodically transform the files.

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Question # 18

A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields For example, a customer record field that is place_id in one database is location_id in another database. The company wants to link customer records across different databases, even when many customer record fields do not match exactly

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

A.

Create an Amazon EMR cluster to process and analyze data in the databases Connect to the Apache Zeppelin notebook, and use the FindMatches transform to find duplicate records in the data.

B.

Create an AWS Glue crawler to crawl the databases. Use the FindMatches transform to find duplicate records in the data Evaluate and tune the transform by evaluating performance and results of finding matches

C.

Create an AWS Glue crawler to crawl the data in the databases Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data

D.

Create an Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook, and use Apache Spark ML to find duplicate records in the data. Evaluate and tune the model by evaluating performance and results of finding duplicates

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Question # 19

A company with a video streaming website wants to analyze user behavior to make recommendations to users in real time Clickstream data is being sent to Amazon Kinesis Data Streams and reference data is stored in Amazon S3 The company wants a solution that can use standard SQL quenes The solution must also provide a way to look up pre-calculated reference data while making recommendations

Which solution meets these requirements?

A.

Use an AWS Glue Python shell job to process incoming data from Kinesis Data Streams Use the Boto3 library to write data to Amazon Redshift

B.

Use AWS Glue streaming and Scale to process incoming data from Kinesis Data Streams Use the AWS Glue connector to write data to Amazon Redshift

C.

Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use a data stream to write results to Amazon Redshift

D.

Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift

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Question # 20

A media content company has a streaming playback application. The company wants to collect and analyze the data to provide near-real-time feedback on playback issues. The company needs to consume this data and return results within 30 seconds according to the service-level agreement (SLA). The company needs the consumer to identify playback issues, such as quality during a specified timeframe. The data will be emitted as JSON and may change schemas over time.

Which solution will allow the company to collect data for processing while meeting these requirements?

A.

Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event trigger an AWS Lambda function to process the data. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.

B.

Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.

C.

Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an event for AWS Lambda to process. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.

D.

Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.

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Question # 21

A retail company is building its data warehouse solution using Amazon Redshift. As a part of that effort, the company is loading hundreds of files into the fact table created in its Amazon Redshift cluster. The company wants the solution to achieve the highest throughput and optimally use cluster resources when loading data into the company’s fact table.

How should the company meet these requirements?

A.

Use multiple COPY commands to load the data into the Amazon Redshift cluster.

B.

Use S3DistCp to load multiple files into the Hadoop Distributed File System (HDFS) and use an HDFS connector to ingest the data into the Amazon Redshift cluster.

C.

Use LOAD commands equal to the number of Amazon Redshift cluster nodes and load the data in parallel into each node.

D.

Use a single COPY command to load the data into the Amazon Redshift cluster.

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Question # 22

A company developed a new elections reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.

Which solution meets these requirements?

A.

Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

B.

Create a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.

C.

Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.

D.

Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

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Question # 23

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync

Which solution will simplify permissions management with minimal development effort?

A.

Set AWS Identity and Access Management (1AM) permissions tor AWS Glue

B.

Use AWS Lake Formation permissions

C.

Manage AWS Glue and S3 permissions by using bucket policies

D.

Use Amazon Cognito user pools.

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Question # 24

A retail company has 15 stores across 6 cities in the United States. Once a month, the sales team requests a visualization in Amazon QuickSight that provides the ability to easily identify revenue trends across cities and stores.The visualization also helps identify outliers that need to be examined with further analysis.

Which visual type in QuickSight meets the sales team's requirements?

A.

Geospatial chart

B.

Line chart

C.

Heat map

D.

Tree map

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Question # 25

A company is designing a data warehouse to support business intelligence reporting. Users will access the executive dashboard heavily each Monday and Friday morning

for I hour. These read-only queries will run on the active Amazon Redshift cluster, which runs on dc2.8xIarge compute nodes 24 hours a day, 7 days a week. There are

three queues set up in workload management: Dashboard, ETL, and System. The Amazon Redshift cluster needs to process the queries without wait time.

What is the MOST cost-effective way to ensure that the cluster processes these queries?

A.

Perform a classic resize to place the cluster in read-only mode while adding an additional node to the cluster.

B.

Enable automatic workload management.

C.

Perform an elastic resize to add an additional node to the cluster.

D.

Enable concurrency scaling for the Dashboard workload queue.

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Question # 26

A data analytics specialist is building an automated ETL ingestion pipeline using AWS Glue to ingest compressed files that have been uploaded to an Amazon S3 bucket. The ingestion pipeline should support incremental data processing.

Which AWS Glue feature should the data analytics specialist use to meet this requirement?

A.

Workflows

B.

Triggers

C.

Job bookmarks

D.

Classifiers

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Question # 27

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing AmazonRedshift cluster. The solution should also require minimal effort and development activity.

Which solution meets these requirements?

A.

Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.

B.

Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.

C.

Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.

D.

Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

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Question # 28

A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.

Which solution allows the data engineering team to efficiently set up the web logging system within AWS?

A.

Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.

B.

Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Data Firehose delivery stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.

C.

Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Configure Splunk as the end destination of the weblogs.

D.

Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Firehose delivery stream to CloudWatch. Configure Amazon DynamoDB as the end destination of the weblogs.

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Question # 29

A bank is building an Amazon S3 data lake. The bank wants a single data repository for customer data needs, such as personalized recommendations. The bank needs to use Amazon Kinesis Data Firehose to ingest customers' personal information, bank accounts, and transactions in near real time from a transactional relational database.

All personally identifiable information (Pll) that is stored in the S3 bucket must be masked. The bank has enabled versioning for the S3 bucket.

Which solution will meet these requirements?

A.

Invoke an AWS Lambda function from Kinesis Data Firehose to mask the PII before Kinesis Data Firehose delivers the data to the S3 bucket.

B.

Use Amazon Macie to scan the S3 bucket. Configure Macie to discover Pll. Invoke an AWS Lambda function from S3 events to mask the Pll.

C.

Configure server-side encryption (SSE) for the S3 bucket. Invoke an AWS Lambda function from S3 events to mask the PII.

D.

Create an AWS Lambda function to read the objects, mask the Pll, and store the objects back with same key. Invoke the Lambda function from S3 events.

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Question # 30

A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solidstate drive (SSD) storage for each node is required to meet the query performance goals.

The company wants to run an additional analysis on a year’s worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week.

What is the MOST cost-effective solution?

A.

Increase the size of the Amazon Redshift cluster to 120 nodes so it has enough storage capacity to hold 1

year of data. Then use Amazon Redshift for the additional analysis.

B.

Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then use Amazon Redshift Spectrum for the additional analysis.

C.

Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then provision a persistent Amazon EMR cluster and use Apache Presto for the additional analysis.

D.

Resize the cluster node type to the dense storage node type (DS2) for an additional 16 TB storage capacity on each individual node in the Amazon Redshift cluster. Then use Amazon Redshift for the additional analysis.

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Question # 31

An IOT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has

its own topic, and each topic has the same number of partitions.

The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data sothat the company can scale

accordingly. The company needs to know which sensor types have the largest values for the following metrics: ByteslnPerSec and MessageslnPerSec.

Which level of monitoring for Amazon MSK will meet these requirements?

A.

DEFAULT level

B.

PER TOPIC PER BROKER level

C.

PER BROKER level

D.

PER TOPIC level

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Question # 32

A company's system operators and security engineers need to analyze activities within specific date ranges of AWS CloudTrail logs. All log files are stored in an Amazon S3 bucket, and the size of the logs is more than 5 T B. The solution must be cost-effective and maximize query performance.

Which solution meets these requirements?

A.

Copy the logs to a new S3 bucket with a prefix structure of . Use the date column as a partition key. Create a table on Amazon Athena based on the objects in the new bucket. Automatically add metadata partitions by using the MSCK REPAIR TABLE command in Athena. Use Athena to query the table and partitions.

B.

Create a table on Amazon Athena. Manually add metadata partitions by using the ALTER TABLE ADD PARTITION statement, and use multiple columns for the partition key. Use Athena to query the table and partitions.

C.

Launch an Amazon EMR cluster and use Amazon S3 as a data store for Apache HBase. Load the logs from the S3 bucket to an HBase table on Amazon EMR. Use Amazon Athena to query the table and partitions.

D.

Create an AWS Glue job to copy the logs from the S3 source bucket to a new S3 bucket and create a table using Apache Parquet file format, Snappy as compression codec, and partition by date. Use Amazon Athena to query the table and partitions.

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Question # 33

A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon Elasticsearch cluster. The validation process needs to receive the posts for a given user in the order they were received. A data analyst has noticed that, during peak hours, the social media platform posts take more than an hour to appear in the Elasticsearch cluster.

What should the data analyst do reduce this latency?

A.

Migrate the validation process to Amazon Kinesis Data Firehose.

B.

Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.

C.

Increase the number of shards in the stream.

D.

Configure multiple Lambda functions to process the stream.

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Question # 34

A bank is using Amazon Managed Streaming for Apache Kafka (Amazon MSK) to populate real-time data into a data lake The data lake is built on Amazon S3, and data must be accessible from the data lake within 24 hours Different microservices produce messages to different topics in the cluster The cluster is created with 8 TB of Amazon Elastic Block Store (Amazon EBS) storage and a retention period of 7 days

The customer transaction volume has tripled recently and disk monitoring has provided an alert that the cluster is almost out of storage capacity

What should a data analytics specialist do to prevent the cluster from running out of disk space1?

A.

Use the Amazon MSK console to triple the broker storage and restart the cluster

B.

Create an Amazon CloudWatch alarm that monitors the KafkaDataLogsDiskUsed metric Automatically flush the oldest messages when the value of this metric exceeds 85%

C.

Create a custom Amazon MSK configuration Set the log retention hours parameter to 48 Update the cluster with the new configuration file

D.

Triple the number of consumers to ensure that data is consumed as soon as it is added to a topic.

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Question # 35

A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.

How should the data be secured?

A.

Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.

B.

Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.

C.

Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.

D.

Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.

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Question # 36

A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.

The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.

Which solution will solve this issue and meet the requirements?

A.

In the Amazon Redshift console, choose to configure cross-Region snapshots and set the destination Region as ap-northeast-1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched in ap-northeast-1.

B.

Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.

C.

Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.

D.

Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.

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Question # 37

A large ecommerce company uses Amazon DynamoDB with provisioned read capacity and auto scaled write capacity to store its product catalog. The company uses Apache HiveQL statements on an Amazon EMR cluster to query the DynamoDB table. After the company announced a sale on all of its products, wait times for each query have increased. The data analyst has determined that the longer wait times are being caused by throttling when querying the table.

Which solution will solve this issue?

A.

Increase the size of the EMR nodes that are provisioned.

B.

Increase the number of EMR nodes that are in the cluster.

C.

Increase the DynamoDB table's provisioned write throughput.

D.

Increase the DynamoDB table's provisioned read throughput.

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Question # 38

A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs.

What is an explanation for this behavior and what is the solution?

A.

There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst

needs to make sure there is only a single shard in the stream and no stream resize runs.

B.

The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.

C.

The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.

D.

The consumer is not processing the parent shard completely before processing the child shards after a stream resize. The data analyst should process the parent shard completely first before processing the child shards.

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Question # 39

A company uses Amazon Redshift as its data warehouse A new table includes some columns that contain sensitive data and some columns that contain non-sensitive data The data in the table eventually will be referenced by several existing queries that run many times each day

A data analytics specialist must ensure that only members of the company's auditing team can read the columns that contain sensitive data All other users must have read-only access to the columns that contain non-sensitive data

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

A.

Grant the auditing team permission to read from the table. Load the columns that contain non-sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.

B.

Grant all users read-only permissions to the columns that contain non-sensitive data Use the GRANT SELECT command to allow the auditing team to access the columns that contain sensitive data

C.

Grant all users read-only permissions to the columns that contain non-sensitive data Attach an 1AM policy to the auditing team with an explicit Allow action that grants access to the columns that contain sensitive data

D.

Grant the auditing team permission to read from the table Create a view of the table that includes the columns that contain non-sensitive data Grant the appropriate users read-only permissions to that view

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Question # 40

A large marketing company needs to store all of its streaming logs and create near-real-time dashboards. The dashboards will be used to help the company make critical business decisions and must be highly available.

Which solution meets these requirements?

A.

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Create the dashboards by using Amazon QuickSight.

B.

Deploy an Amazon Redshift cluster with at least three nodes in a VPC that spans two Availability Zones. Store the streaming logs and use the Redshift cluster as a source to create the dashboards by using Amazon QuickSight.

C.

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Every time a new log is added in the bucket, invoke an AWS Lambda function to update the dashboards in Amazon QuickSight.

D.

Store the streaming logs in Amazon OpenSearch Service deployed across three Availability Zones and with three dedicated master nodes. Create the dashboards by using OpenSearch Dashboards.

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Question # 41

A retail company stores order invoices in an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster Indices on the cluster are created monthly Once a new month begins, no new writes are made to any of the indices from the previous months The company has been expanding the storage on the Amazon OpenSearch Service {Amazon Elasticsearch Service) cluster to avoid running out of space, but the company wants to reduce costs Most searches on the cluster are on the most recent 3 months of data while the audit team requires infrequent access to older data to generate periodic reports The most recent 3 months of data must be quickly available for queries, but the audit team can tolerate slower queries if the solution saves on cluster costs

Which of the following is the MOST operationally efficient solution to meet these requirements?

A.

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to store the indices in Amazon S3 Glacier When the audit team requires the archived data restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster

B.

Archive indices that are older than 3 months by taking manual snapshots and storing the snapshots in Amazon S3 When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster

C.

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage

D.

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage When the audit team requires the older data: migrate the indices in UltraWarm storage back to hot storage

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Question # 42

A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.

The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.

Which configuration will provide the MOST cost-effective solution that meets these requirements?

A.

Load the data into an Amazon Redshift cluster by using the COPY command. Configure 50 author users and 1,000 reader users. Use QuickSight Enterprise edition. Configure an Amazon Redshift data source with a direct query option.

B.

Use QuickSight Standard edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source with a direct query option.

C.

Use QuickSight Enterprise edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source and import the data into SPICE. Automatically refresh every 24 hours.

D.

Use QuickSight Enterprise edition. Configure 1 administrator and 1,000 reader users. Configure an S3 data source and import the data into SPICE. Automatically refresh every 24 hours.

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Question # 43

An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.

A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.

How should the data analyst meet this requirement while minimizing costs?

A.

Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.

B.

Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the

.csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the file’s S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.

C.

Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.

D.

Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.

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Question # 44

A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records.

Which solution will accomplish this task?

A.

An AWS Glue ETL job with the FindMatches transform

B.

Amazon Kendra

C.

Amazon SageMaker Ground Truth

D.

An AWS Glue ETL job with the ResolveChoice transform

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Question # 45

A manufacturing company uses Amazon S3 to store its data. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format. The company has set a deadline for a consultant to build a data lake.

How should the consultant create the MOST cost-effective solution that meets these requirements?

A.

Run Lake Formation blueprints to move the data to Lake Formation. Once Lake Formation has the data, apply permissions on Lake Formation.

B.

To create the data catalog, run an AWS Glue crawler on the existing Parquet data. Register the Amazon S3 path and then apply permissions through Lake Formation to provide granular-level security.

C.

Install Apache Ranger on an Amazon EC2 instance and integrate with Amazon EMR. Using Ranger policies, create role-based access control for the existing data assets in Amazon S3.

D.

Create multiple IAM roles for different users and groups. Assign IAM roles to different data assets in Amazon S3 to create table-based and column-based access controls.

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Question # 46

A gaming company is building a serverless data lake. The company is ingesting streaming data into Amazon Kinesis Data Streams and is writing the data to Amazon S3 through Amazon Kinesis Data Firehose. The company is using 10 MB as the S3 buffer size and is using 90 seconds as the buffer interval. The company runs an AWS Glue ET L job to merge and transform the data to a different format before writing the data back to Amazon S3.

Recently, the company has experienced substantial growth in its data volume. The AWS Glue ETL jobs are frequently showing an OutOfMemoryError error.

Which solutions will resolve this issue without incurring additional costs? (Select TWO.)

A.

Place the small files into one S3 folder. Define one single table for the small S3 files in AWS Glue Data Catalog. Rerun the AWS Glue ET L jobs against this AWS Glue table.

B.

Create an AWS Lambda function to merge small S3 files and invoke them periodically. Run the AWS Glue ETL jobs after successful completion of the Lambda function.

C.

Run the S3DistCp utility in Amazon EMR to merge a large number of small S3 files before running the AWS Glue ETL jobs.

D.

Use the groupFiIes setting in the AWS Glue ET L job to merge small S3 files and rerun AWS Glue E TL jobs.

E.

Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds.

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Question # 47

A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.

Which solution meets these requirements?

A.

Create IAM groups and resource tags for each team within the company. Set up IAM policies that control

user access and actions on the Data Catalog resources.

B.

Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.

C.

Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.

D.

Create Athena query groups for each team within the company and assign users to the groups.

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Question # 48

A network administrator needs to create a dashboard to visualize continuous network patterns over time in a company's AWS account. Currently, the company has VPC Flow Logs enabled and is publishing this data to Amazon CloudWatch Logs. To troubleshoot networking issues quickly, the dashboard needs to display the new data in near-real time.

Which solution meets these requirements?

A.

Create a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes the data to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

B.

Create an export task from CloudWatch Logs to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

C.

Create a CloudWatch Logs subscription that uses an AWS Lambda function to stream the CloudWatch Logs data directly into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

D.

Create a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes to an Amazon Kinesis data stream to deliver the data into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

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Question # 49

A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.

Which solution meets these requirements?

A.

Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

B.

Use Apache Spark Streaming on Amazon EMR to read the data in near-real time. Develop a custom application for the dashboard by using D3.js.

C.

Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluster. Visualize the data by using a Kibana dashboard.

D.

Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

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Question # 50

A central government organization is collecting events from various internal applications using Amazon Managed Streaming for Apache Kafka (Amazon MSK). The organization has configured a separate Kafka topic for each application to separate the data. For security reasons, the Kafka cluster has been configured to only allow TLS encrypted data and it encrypts the data at rest.

A recent application update showed that one of the applications was configured incorrectly, resulting in writing data to a Kafka topic that belongs to another application. This resulted in multiple errors in the analytics pipeline as data from different applications appeared on the same topic. After this incident, the organization wants to prevent applications from writing to a topic different than the one they should write to.

Which solution meets these requirements with the least amount of effort?

A.

Create a different Amazon EC2 security group for each application. Configure each security group to have access to a specific topic in the Amazon MSK cluster. Attach the security group to each application based on the topic that the applications should read and write to.

B.

Install Kafka Connect on each application instance and configure each Kafka Connect instance to write to a specific topic only.

C.

Use Kafka ACLs and configure read and write permissions for each topic. Use the distinguished name of the clients' TLS certificates as the principal of the ACL.

D.

Create a different Amazon EC2 security group for each application. Create an Amazon MSK cluster and Kafka topic for each application. Configure each security group to have access to the specific cluster.

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Question # 51

An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tool.

The company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: “Creating a connection to your data source timed out.”

How should the data analytics specialist resolve this error?

A.

Grant the SELECT permission on Amazon Redshift tables.

B.

Add the QuickSight IP address range into the Amazon Redshift security group.

C.

Create an IAM role for QuickSight to access Amazon Redshift.

D.

Use a QuickSight admin user for creating the dataset.

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Question # 52

A company uses Amazon Redshift as its data warehouse. The Redshift cluster is not encrypted. A data analytics specialist needs to use hardware security module (HSM) managed encryption keys to encrypt the data that is stored in the Redshift cluster.

Which combination of steps will meet these requirements? (Select THREE.)

A.

Stop all write operations on the source cluster. Unload data from the source cluster.

B.

Copy the data to a new target cluster that is encrypted with AWS Key Management Service (AWS KMS).

C.

Modify the source cluster by activating AWS CloudHSM encryption. Configure Amazon Redshift to automatically migrate data to a new encrypted cluster.

D.

Modify the source cluster by activating encryption from an external HSM. Configure Amazon Redshift to automatically migrate data to a new encrypted cluster.

E.

Copy the data to a new target cluster that is encrypted with an HSM from AWS CloudHSM.

F.

Rename the source cluster and the target cluster after the migration so that the target cluster is using the original endpoint.

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Question # 53

A marketing company is storing its campaign response data in Amazon S3. A consistent set of sources has generated the data for each campaign. The data is saved into Amazon S3 as .csv files. A business analyst will use Amazon Athena to analyze each campaign’s data. The company needs the cost of ongoing data analysis with Athena to be minimized.

Which combination of actions should a data analytics specialist take to meet these requirements? (Choose two.)

A.

Convert the .csv files to Apache Parquet.

B.

Convert the .csv files to Apache Avro.

C.

Partition the data by campaign.

D.

Partition the data by source.

E.

Compress the .csv files.

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Question # 54

A utility company wants to visualize data for energy usage on a daily basis in Amazon QuickSight A data analytics specialist at the company has built a data pipeline to collect and ingest the data into Amazon S3 Each day the data is stored in an individual csv file in an S3 bucket This is an example of the naming structure

20210707_datacsv 20210708_datacsv

To allow for data querying in QuickSight through Amazon Athena the specialist used an AWS Glue crawler to create a table with the path "s3 //powertransformer/20210707_data csv" However when the data is queried, it returns zero rows

How can this issue be resolved?

A.

Modify the IAM policy for the AWS Glue crawler to access Amazon S3.

B.

Ingest the files again.

C.

Store the files in Apache Parquet format.

D.

Update the table path to "s3://powertransformer/".

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Question # 55

A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.

Which architectural pattern meets company’s requirements?

A.

Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node. Configure

the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.

B.

Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view. Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.

C.

Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view. Run two separate EMR clusters in two different Availability Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.

D.

Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view. Create a primary EMR HBase cluster with multiple master nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.

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Question # 56

A team of data scientists plans to analyze market trend data for their company’s new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.

Which solution meets these requirements?

A.

Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.

B.

Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.

C.

Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.

D.

Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.

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Question # 57

A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.

The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.

Which solution will meet these requirements?

A.

Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.

B.

Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.

C.

Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.

D.

Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.

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Question # 58

A company’s marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:

  • The data size is approximately 32 TB uncompressed.
  • There is a low volume of single-row inserts each day.
  • There is a high volume of aggregation queries each day.
  • Multiple complex joins are performed.
  • The queries typically involve a small subset of the columns in a table.

Which storage service will provide the MOST performant solution?

A.

Amazon Aurora MySQL

B.

Amazon Redshift

C.

Amazon Neptune

D.

Amazon Elasticsearch

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Question # 59

An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.

Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

A.

Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.

B.

Use an S3 bucket in the same account as Athena.

C.

Compress the objects to reduce the data transfer I/O.

D.

Use an S3 bucket in the same Region as Athena.

E.

Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.

F.

Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

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Question # 60

A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.

Which solution meets these requirements?

A.

Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the data catalog in Aurora. Schedule the Lambda functions periodically.

B.

Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata changes. Schedule the crawlers periodically to update the metadata catalog.

C.

Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalog. Schedule the Lambda functions periodically.

D.

Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalog. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.

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Question # 61

A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient’s protected health information (PHI) from the streaming data and store the data in durable storage.

Which solution meets these requirements with the least operational overhead?

A.

Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PHI. Write the data in Amazon S3.

B.

Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.

C.

Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.

D.

Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.

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