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AWS Certified Machine Learning Engineer - Associate (AWS-MLAE) Practice Questions: Machine Learning Engineering Domain

Test your AWS Certified Machine Learning Engineer - Associate (AWS-MLAE) knowledge with 5 practice questions from the Machine Learning Engineering domain. Includes detailed explanations and answers.

AWS Certified Machine Learning Engineer - Associate (AWS-MLAE) Practice Questions

Master the Machine Learning Engineering Domain

Test your knowledge in the Machine Learning Engineering domain with these 5 practice questions. Each question is designed to help you prepare for the AWS Certified Machine Learning Engineer - Associate (AWS-MLAE) certification exam with detailed explanations to reinforce your learning.

Question 1

You need to deploy a machine learning model that can handle dynamic scaling based on traffic demand. Which SageMaker deployment option should you choose?

A) SageMaker Batch Transform

B) SageMaker Real-Time Endpoint with Auto Scaling

C) SageMaker Edge Manager

D) SageMaker Neo

Show Answer & Explanation

Correct Answer: B

Explanation: SageMaker Real-Time Endpoint with Auto Scaling is the appropriate choice for deploying a model that needs to handle dynamic scaling based on traffic demand. It allows the endpoint to automatically adjust the number of instances based on the load. Batch Transform is for batch processing, Edge Manager is for managing models on edge devices, and Neo is for optimizing models for edge deployment.

Question 2

For a machine learning model deployed on AWS that needs to comply with data privacy regulations, which SageMaker feature can help ensure transparency in model predictions?

A) SageMaker Clarify

B) SageMaker Debugger

C) SageMaker Neo

D) SageMaker Ground Truth

Show Answer & Explanation

Correct Answer: A

Explanation: SageMaker Clarify provides tools to improve the transparency of machine learning models. It helps detect bias in datasets and models and explains model predictions, which is crucial for compliance with data privacy regulations.

Question 3

Which AWS service should you use to create a managed data lake for your machine learning project, enabling you to store, catalog, and analyze large datasets efficiently?

A) Amazon S3

B) AWS Lake Formation

C) Amazon RDS

D) AWS Glue

Show Answer & Explanation

Correct Answer: B

Explanation: AWS Lake Formation is designed to create, secure, and manage data lakes. It simplifies the process of setting up a data lake, allowing you to store and catalog data from various sources efficiently. Amazon S3 is used for storage but doesn't provide data lake management features. Amazon RDS is for relational databases, and AWS Glue is a data integration service that can be used in conjunction with a data lake.

Question 4

Which AWS service would you use to integrate a machine learning model with a web application for real-time prediction?

A) Amazon API Gateway

B) AWS Step Functions

C) Amazon Kinesis

D) Amazon S3

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon API Gateway enables developers to create, publish, maintain, monitor, and secure APIs at any scale. It is suitable for integrating machine learning models with web applications to provide real-time predictions.

Question 5

You need to prepare a large dataset stored in Amazon S3 for training a machine learning model in SageMaker. Which AWS service can automate the data preprocessing tasks such as normalization and feature engineering?

A) AWS Glue

B) Amazon SageMaker Data Wrangler

C) Amazon Athena

D) AWS Lambda

Show Answer & Explanation

Correct Answer: B

Explanation: Amazon SageMaker Data Wrangler is specifically designed to simplify data preparation tasks such as normalization and feature engineering for machine learning workflows. AWS Glue is used for ETL tasks but is not specialized for ML-specific preprocessing. Amazon Athena is for querying data, and AWS Lambda is for running code in response to events.

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🧠 Practice Questions for AWS-MLAE Exam

About AWS Certified Machine Learning Engineer - Associate (AWS-MLAE) Certification

The AWS Certified Machine Learning Engineer - Associate (AWS-MLAE) certification validates your expertise in machine learning engineering and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.