Amazon Web Services AIF-C01 Exam Dumps - Actual Questions Answers

The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language. AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Amazon Transcribe:

"Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users."

(Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe)

Detailed Explanation:

Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.

Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.

Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.

Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.

Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.

Amazon SageMaker Feature Store:

A fully managed repository for storing, sharing, and managing machine learning features across different teams and models.

It enables collaboration and reuse of features, ensuring consistent data usage and reducing redundancy.

Why Option A is Correct:

Centralized Feature Management: Provides a central repository for managing features, making it easier to share them across teams.

Collaboration and Reusability: Improves efficiency by allowing teams to reuse existing features instead of creating them from scratch.

Why Other Options are Incorrect:

B. SageMaker Data Wrangler: Helps with data preparation and analysis but does not provide a centralized feature store.

C. SageMaker Clarify: Used for bias detection and explainability, not for managing variables across teams.

D. SageMaker Model Cards: Provide model documentation, not feature management.

Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations (embeddings) of data are often used to capture semantic meaning.

Scalable index management and nearest neighbor search capability are the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.

Here is why option C is the correct answer:

Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost-effective and performance-optimized way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.

Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor search capability allows for fast and efficient searches over vector data. This is essential for applications like product recommendation engines, where the system needs to quickly find the most similar items based on a user's query or behavior.

AWS AI Practitioner References:

According to AWS documentation, OpenSearch Service's support for nearest neighbor search using vector embeddings is a key feature for companies building machine learning applications that require similarity search.

The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large datasets, ensuring high performance even with large-scale vector data.

The other options do not directly relate to building vector database applications:

A. Integration with Amazon S3 for object storage is about storing data objects, not vector-based searching or indexing.

B. Support for geospatial indexing and queries is related to location-based data, not vectors used in machine learning.

D. Ability to perform real-time analysis on streaming data relates to analyzing incoming data streams, which is different from the vector search capabilities.