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AWS Certified Machine Learning Engineer - Associate

AWS ML Engineer Associate Practice Test

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Questions updated at Jul 10, 2026, 12:01 AM CDT

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AWS ML Engineer Associate

AWS Certified Machine Learning Engineer - Associate

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Today's 10 AWS ML Engineer Associate questions

Use this AWS ML Engineer Associate practice test to review AWS Certified Machine Learning Engineer Associate. Questions rotate daily and each explanation links to the source used to validate the answer.

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Question 1 of 10
Objective MLEA-06 Monitoring and Maintenance

A production model needs data quality drift checks against expected statistics and constraints. Which SageMaker setup provides the baseline for those checks?

Concept tested: Monitoring and Maintenance (MLEA-06)
Source:Monitor models in production with Amazon SageMaker Model Monitor
Question 2 of 10
Objective MLEA-04 Workflow Automation

A SageMaker pipeline should notify Slack after evaluation without using a training instance for notification code. Which step design is appropriate?

Concept tested: Workflow Automation (MLEA-04)
Question 3 of 10
Objective MLEA-01 Data Preparation and Feature Engineering

A model performs well offline but degrades in production because training features and live-serving features are assembled differently. Which design choice reduces that risk?

Concept tested: Data Preparation and Feature Engineering (MLEA-01)
Question 4 of 10
Objective MLEA-09 Certification Scope and Production Ownership

An engineer is taking ownership of an ML model after experimentation. Which responsibility best describes production ownership on AWS?

Concept tested: Certification Scope and Production Ownership (MLEA-09)
Question 5 of 10
Objective MLEA-03 Model Training and Tuning

A regulated team must explain which features most influenced an individual prediction before approving a model. Which SageMaker capability is most relevant?

Concept tested: Model Training and Tuning (MLEA-03)
Question 6 of 10
Objective MLEA-08 Well-Architected ML Systems

An ML platform team wants to reduce unauthorized access to training data, models, and deployment resources. Which architecture practice should it apply?

Concept tested: Well-Architected ML Systems (MLEA-08)
Question 7 of 10
Objective MLEA-02 Deployment Patterns

A team needs to host many related models behind one SageMaker endpoint and load the requested model on demand from S3. Which hosting pattern fits?

Concept tested: Deployment Patterns (MLEA-02)
Question 8 of 10
Objective MLEA-07 Responsible AI

An ML system will influence important decisions for users. Which responsible AI control should the team include?

Concept tested: Responsible AI (MLEA-07)
Question 9 of 10
Objective MLEA-05 Deployment Guardrails

A team wants to deploy a new SageMaker endpoint version gradually, monitor health, and keep a safer path back to the prior version. Which deployment feature supports that rollout?

Concept tested: Deployment Guardrails (MLEA-05)
Question 10 of 10
Objective MLEA-06 Monitoring and Maintenance

A bank already checked bias before release, but regulators now want proof that fairness metrics are still being tracked in production over time. Which SageMaker feature best addresses that requirement?

Concept tested: Monitoring and Maintenance (MLEA-06)
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Official exam resources

Use these official AWS resources alongside the daily practice set. They cover the provider's own exam page, study guide, or prep material.

Certification AWS Certified Machine Learning Engineer - Associate Docs Amazon SageMaker AI documentation

Need adjacent AWS practice pages too? AWS practice hub.

Source-backed answer review

The free daily AWS ML Engineer Associate set includes crawlable question text, answer choices, correct answer labels, objective mapping, and source links. Only the first SEO card includes answer explanations. Pro-only bank questions stay locked; this section mirrors only the 10 free daily questions already shown on this page.

Question 1 A production model needs data quality drift checks against expected statistics and constraints. Which SageMaker setup provides the baseline for those checks?

Answer choices

  1. A. A multi-model endpoint cache policy
  2. B. A Model Monitor baseline with statistics and constraints
  3. C. A Route 53 health check only
  4. D. A serverless concurrency limit

Correct answer

A Model Monitor baseline with statistics and constraints

Model Monitor uses a baseline to compare production data against expected distributions and rules. A Model Monitor baseline with statistics and constraints is correct because it provides the reference used for drift and data-quality checks. Cache policies, DNS health checks, and concurrency limits do not define data-quality expectations.

Wrong-answer review

  • A. A multi-model endpoint cache policy: A multi-model endpoint cache policy affects model loading behavior, not data-quality drift detection.
  • C. A Route 53 health check only: A Route 53 health check only checks endpoint reachability or health, not model input quality.
  • D. A serverless concurrency limit: A serverless concurrency limit controls capacity and does not define monitoring constraints.

Objective/domain: Monitoring and Maintenance (MLEA-06)

Source: Monitor models in production with Amazon SageMaker Model Monitor

Question 2 A SageMaker pipeline should notify Slack after evaluation without using a training instance for notification code. Which step design is appropriate?

Answer choices

  1. A. Use a GPU TrainingStep for a Slack script
  2. B. Use LambdaStep to invoke Lambda webhook code
  3. C. Trigger Slack from S3 event notifications
  4. D. Subscribe SNS directly to Model Registry events

Correct answer

Use LambdaStep to invoke Lambda webhook code

Objective/domain: Workflow Automation (MLEA-04)

Source: Run AWS Lambda Functions in a SageMaker Pipeline

Question 3 A model performs well offline but degrades in production because training features and live-serving features are assembled differently. Which design choice reduces that risk?

Answer choices

  1. A. Remove all feature validation to speed up ingestion
  2. B. Increase the endpoint timeout
  3. C. Switch every endpoint to serverless inference
  4. D. Use the same managed feature definitions for both offline training and online inference

Correct answer

Use the same managed feature definitions for both offline training and online inference

Objective/domain: Data Preparation and Feature Engineering (MLEA-01)

Source: Amazon SageMaker Feature Store

Question 4 An engineer is taking ownership of an ML model after experimentation. Which responsibility best describes production ownership on AWS?

Answer choices

  1. A. How to remove all governance checks to ship faster
  2. B. How to deploy, automate, monitor, and maintain the model reliably on AWS
  3. C. How to avoid documenting assumptions or limitations
  4. D. How to keep the model only in a local notebook

Correct answer

How to deploy, automate, monitor, and maintain the model reliably on AWS

Objective/domain: Certification Scope and Production Ownership (MLEA-09)

Source: AWS Certified Machine Learning Engineer - Associate

Question 5 A regulated team must explain which features most influenced an individual prediction before approving a model. Which SageMaker capability is most relevant?

Answer choices

  1. A. Batch Transform
  2. B. SageMaker Clarify explainability processing
  3. C. A multi-container endpoint
  4. D. Serverless inference scaling

Correct answer

SageMaker Clarify explainability processing

Objective/domain: Model Training and Tuning (MLEA-03)

Source: Run an Amazon SageMaker Clarify processing job

Question 6 An ML platform team wants to reduce unauthorized access to training data, models, and deployment resources. Which architecture practice should it apply?

Answer choices

  1. A. Hyperparameter exploration
  2. B. Hybrid search ranking
  3. C. Batch transform partitioning
  4. D. Least-privilege security and controlled access

Correct answer

Least-privilege security and controlled access

Objective/domain: Well-Architected ML Systems (MLEA-08)

Source: AWS Well-Architected Machine Learning Lens

Question 7 A team needs to host many related models behind one SageMaker endpoint and load the requested model on demand from S3. Which hosting pattern fits?

Answer choices

  1. A. One dedicated single-model endpoint per model
  2. B. Only Batch Transform
  3. C. Only Feature Store offline access
  4. D. Multi-model endpoints

Correct answer

Multi-model endpoints

Objective/domain: Deployment Patterns (MLEA-02)

Source: Multi-model endpoints - Amazon SageMaker AI

Question 8 An ML system will influence important decisions for users. Which responsible AI control should the team include?

Answer choices

  1. A. Disable auditing to reduce latency
  2. B. Provide explainability and appropriate human oversight for important decisions
  3. C. Hide model reasoning to protect internal efficiency
  4. D. Remove all feedback channels after deployment

Correct answer

Provide explainability and appropriate human oversight for important decisions

Objective/domain: Responsible AI (MLEA-07)

Source: Responsible AI on AWS

Question 9 A team wants to deploy a new SageMaker endpoint version gradually, monitor health, and keep a safer path back to the prior version. Which deployment feature supports that rollout?

Answer choices

  1. A. Manual console edits with no staged rollout
  2. B. Blue/green deployment guardrails
  3. C. An offline feature store export
  4. D. A batch transform job

Correct answer

Blue/green deployment guardrails

Objective/domain: Deployment Guardrails (MLEA-05)

Source: Deploy models using deployment guardrails

Question 10 A bank already checked bias before release, but regulators now want proof that fairness metrics are still being tracked in production over time. Which SageMaker feature best addresses that requirement?

Answer choices

  1. A. A blue/green traffic policy
  2. B. An online feature store key lookup
  3. C. A batch transform output prefix
  4. D. SageMaker Clarify bias drift monitoring

Correct answer

SageMaker Clarify bias drift monitoring

Objective/domain: Monitoring and Maintenance (MLEA-06)

Source: Monitor bias drift for models in production with SageMaker Clarify

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