The correct answer is B because Amazon CloudWatch provides monitoring capabilities that include tracking usage metrics for Amazon Comprehend endpoints, such as invocation counts. You can configure CloudWatch to collect these metrics and create custom dashboards or alarms to report when an endpoint has zero usage over a period (e.g., 15 days).
From AWS documentation:
"Amazon CloudWatch enables you to collect and track metrics for Comprehend endpoints, create alarms, and automatically react to changes in your AWS resources." This allows automated reporting and alerting for underused or idle endpoints.
Explanation of other options:
A). AWS Trusted Advisor focuses on general AWS resource optimization, security, and limits but does not track endpoint usage.
C). AWS CloudTrail tracks API calls but does not provide time-based monitoring or usage analysis over time.
D). AWS Config is used to track configuration changes and compliance, not endpoint usage metrics.
Referenced AWS AI/ML Documents and Study Guides:
Amazon CloudWatch Metrics for Amazon Comprehend
AWS Certified Machine Learning Specialty Guide - Monitoring and Logging Section AWS Cloud Operations Guide - Resource Utilization Monitoring

AWS Glue is the correct service for transforming petabytes of unstructured data into a structured format suitable for machine learning tasks.
AWS Glue:
A fully managed extract, transform, and load (ETL) service that makes it easy to prepare and transform unstructured data into a structured format.
Provides a range of tools for cleaning, enriching, and cataloging data, making it ready for data scientists to use in ML models.
Why Option D is Correct:
Data Transformation: AWS Glue can handle large volumes of data and transform unstructured data into structured formats efficiently.
Integrated ML Support: Glue integrates with other AWS services to support ML workflows.
Why Other Options are Incorrect:
A . Amazon Lex: Used for building chatbots, not for data transformation.
B . Amazon Rekognition: Used for image and video analysis, not for data transformation.
C . Amazon Kinesis Data Streams: Handles real-time data streaming, not suitable for batch transformation of large volumes of unstructured data.

Amazon SageMaker Canvas is a visual, no-code tool for building and deploying ML models. According to the official SageMaker Canvas documentation:
"SageMaker Canvas provides a visual point-and-click interface that allows business analysts to generate accurate ML predictions without writing any code."

The correct answer is C because Amazon Bedrock with On-Demand Throughput is designed for variable or unpredictable workloads. It allows you to pay only for what you use, which is ideal for early-stage or pilot-phase generative AI applications.
From AWS documentation:
"On-Demand Throughput in Amazon Bedrock provides flexibility and cost-efficiency for unpredictable or low-volume generative AI applications. You are charged based on the number of input/output tokens processed, with no need to provision dedicated capacity." Explanation of other options:
A . Using GPU-powered EC2 instances requires managing infrastructure and is generally more expensive, especially for inconsistent usage.
B . Provisioned Throughput is best suited for predictable, high-volume production workloads where guaranteed throughput is needed.
D . SageMaker JumpStart is for training and deploying ML models, not for efficient inference of foundation models.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Pricing and Throughput Models
AWS Generative AI Best Practices - Cost Optimization
AWS ML Specialty Guide - Bedrock Deployment Models

The correct answer is B - Data preprocessing, which involves cleaning, filtering, and transforming raw data before model training. AWS defines preprocessing as the step where irrelevant or poor-quality data (e.g., excessively short or misrecorded audio) is removed or normalized. The described task-filtering recordings by duration and language-is a textbook example of preprocessing since it prepares the dataset for downstream feature extraction. Feature engineering would occur afterward when converting audio signals into numerical representations (like Mel-frequency coefficients). Model training and data collection come after and before preprocessing respectively. Proper preprocessing ensures data consistency, reduces noise, and improves model accuracy, especially in audio-based healthcare models where clarity and consistency are critical.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - Data Preprocessing and Cleaning
Amazon SageMaker Data Wrangler Documentation - Data Preparation Best Practices