AWS Certified Machine Learning Engineer - Associate Practice Questions: AWS ML Services Integration Domain

Correct Answer: C

Explanation: Amazon S3 Glacier is a cost-effective storage option for data that is infrequently accessed, making it suitable for archival storage. Amazon S3 Standard (A) is more expensive and intended for frequently accessed data. Amazon S3 Intelligent-Tiering (B) automatically moves data between two access tiers when access patterns change. Amazon EFS (D) is a file storage service for use with Amazon EC2 and is not optimized for cost-effective storage of infrequently accessed data.

Correct Answer: A

Explanation: Amazon EMR is a managed cluster platform that simplifies running big data frameworks like Apache Hadoop and Apache Spark, which are ideal for processing large datasets in parallel. AWS Lambda is more suited for event-driven processing, Amazon RDS is for relational databases, and AWS Glue is an ETL service but not specifically optimized for parallel processing of large datasets.

Correct Answer: A

Explanation: A is correct because using SageMaker's built-in algorithms and enabling automatic model tuning will help optimize the hyperparameters and improve the model's performance efficiently. B is incorrect as it may lead to unnecessary costs. C is incorrect because training locally does not leverage SageMaker's capabilities. D is incorrect as Ground Truth is used for data labeling, which may not be necessary if the data is already labeled.

Correct Answer: A

Explanation: Amazon SageMaker Training is specifically designed for training machine learning models at scale, supporting distributed training across multiple instances, which is ideal for deep learning models. AWS Lambda is for serverless functions, Amazon EC2 Auto Scaling is for scaling EC2 instances, and AWS Batch is for batch computing jobs, not specifically optimized for ML training.

Correct Answer: C

Explanation: Amazon SageMaker Feature Store is a fully managed repository that allows you to create, manage, and store features for machine learning models. Amazon RDS (A) is a relational database service. Amazon DynamoDB (B) is a NoSQL database service. Amazon Redshift (D) is a data warehousing service.

Correct Answer: C

Explanation: Enabling Auto Scaling for the endpoint allows AWS SageMaker to automatically adjust the number of instances based on the incoming traffic, ensuring efficient resource utilization and cost-effectiveness. Specifying a fixed number of instances does not allow for scaling, multi-model endpoints are used for serving multiple models, and deploying in a single AZ does not address scaling needs.

Correct Answer: B

Explanation: AWS SageMaker Neo is a service that optimizes machine learning models to run faster on a variety of hardware platforms by compiling them into an efficient format. It is not used for hyperparameter tuning, data preprocessing, or data labeling (these are covered by other SageMaker features).

Correct Answer: D

Explanation: The ml.r5 instance family is optimized for memory-intensive applications, making ml.r5.2xlarge a suitable choice for memory-intensive training jobs in SageMaker. The ml.t3.medium is a general-purpose instance, ml.p3.2xlarge is optimized for GPU-based training, and ml.m5.4xlarge is a general-purpose compute-optimized instance.

Correct Answer: C

Explanation: Amazon SageMaker Debugger provides real-time insights into model training by capturing and analyzing training metrics, helping to detect issues like overfitting or vanishing gradients. It does not reduce training time, tune hyperparameters, or scale the training job.

Correct Answer: B

Explanation: AWS Glue is a fully managed ETL service that makes it easy to prepare and transform data for analytics and machine learning. It can handle large datasets efficiently. Amazon Athena is used for querying data in S3 using SQL. Amazon DynamoDB is a NoSQL database service, and AWS Elastic Beanstalk is used for deploying web applications, not data transformation.