AWS MLA-C01 Exam Practice Questions, practice question pack
AWS · MLA-C01

AWS MLA-C01 Exam Practice Questions

271 questionsInstant PDF downloadUpdated September 2026

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Three of the 271 questions in this pack

Question 1

CASE STUDY

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company needs to use the central model registry to manage different versions of models in the application.

Which action will meet this requirement with the LEAST operational overhead?

  1. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
  2. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
  3. Use the SageMaker Model Registry and model groups to catalog the models.
  4. Use the SageMaker Model Registry and unique tags for each model version.
Show answer and explanation

Correct answer: C. Use the SageMaker Model Registry and model groups to catalog the models.

to catalog the models. SageMaker Model Registry with model groups is the purpose-built AWS service for managing model versions and metadata with minimal operational overhead. It provides native integration with SageMaker's ML lifecycle, automatic versioning, and approval workflows. ECR repositories are container registries not designed for model metadata management, and tagging alone (options B and D) lacks the governance and cataloging features of the Model Registry.

Why the other options are wrong

  • A. ECR repositories are for container images, not optimized for ML model versioning and governance.
  • B. ECR with tags requires manual management and lacks SageMaker's model-specific features like approval status and lineage tracking.
  • D. Generic tagging in Model Registry lacks the organizational structure that model groups provide for managing related model versions.

Question 2

CASE STUDY

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company is experimenting with consecutive training jobs.

How can the company MINIMIZE infrastructure startup times for these jobs?

  1. Use Managed Spot Training.
  2. Use SageMaker managed warm pools.
  3. Use SageMaker Training Compiler.
  4. Use the SageMaker distributed data parallelism (SMDDP) library.
Show answer and explanation

Correct answer: B. Use SageMaker managed warm pools.

SageMaker managed warm pools maintain pre-initialized compute resources between training jobs, eliminating the startup overhead of provisioning and initializing instances. This directly addresses minimizing infrastructure startup times for consecutive jobs. Managed Spot Training reduces costs but not startup time. Training Compiler optimizes code execution speed, not infrastructure provisioning. SMDDP is for distributed training parallelism, not startup optimization.

Why the other options are wrong

  • A. Managed Spot Training reduces costs through interruption-prone instances but does not minimize startup times.
  • C. Training Compiler optimizes training job execution speed, not the infrastructure provisioning startup phase.
  • D. Distributed data parallelism is for scaling training across multiple instances, not reducing startup times.

Question 3

CASE STUDY

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.

Which solution will meet this requirement?

  1. Use SageMaker Experiments to facilitate the approval process during model registration.
  2. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  3. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  4. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
Show answer and explanation

Correct answer: D. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

registered, use the AWS SDK to change the approval status to "Approved." A model version registered in the SageMaker Model Registry starts with a PendingManualApproval status, so it cannot be deployed until a reviewer acts. With SageMaker Pipelines, the workflow registers the version, and the reviewer calls the AWS SDK UpdateModelPackage API to set the status to Approved. Deployment steps are gated on that status, so only models a person has approved reach production endpoints. Experiments, ML Lineage Tracking, and Model Monitor do not control this status.

Why the other options are wrong

  • A. SageMaker Experiments organizes and compares training runs; it does not gate model deployment.
  • B. ML Lineage Tracking records model relationships and data provenance; it does not gate deployment on approval.
  • C. Model Monitor detects data drift and quality issues on endpoints; it does not manage registry approval status.

See all 10 free questions Get the full pack, US$39

271 practice questions for AWS Certified Machine Learning Engineer, Associate (MLA-C01), with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 271 questions across all four MLA-C01 domains
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A failed MLA-C01 attempt costs another US$150, plus the time it takes to restudy. This pack is US$39, paid once, and refunded if you fail.

Try 10 questions free before you buy.

Last updated September 2026 · 271 questions

What makes the MLA-C01 hard

MLA-C01 is an MLOps exam wearing a machine learning badge. It does not ask you to derive algorithms; it drops you into a SageMaker workflow and asks how you would prepare the data, deploy the endpoint and keep the model honest in production. Feature engineering, SageMaker training and hyperparameter tuning, real-time versus batch versus serverless inference, model monitoring and drift detection, ML pipeline orchestration, and IAM and VPC security for ML workloads are the calls a real ML engineer makes every day.

65 questions in 130 minutes, 720 out of 1000 to pass. This pack has 271 practice questions across all four domains, so the SageMaker-heavy scenarios, the newer question formats and the cost-effectiveness trade-offs are familiar before you sit down.

About the exam

MLA-C01 certifies ML engineers who build, deploy and operationalise machine learning workloads on AWS, covering data preparation, model development, deployment and monitoring with Amazon SageMaker and the surrounding AWS data and security stack. AWS recommends at least one year of ML engineering experience. Valid for three years.

Exam domains

  • Data preparation for machine learning: 28%
  • ML model development: 26%
  • Deployment and orchestration of ML workflows: 22%
  • ML solution monitoring, maintenance, and security: 24%

65 questions, 130 minutes, pass mark 720 out of 1000, US$150 per attempt, Pearson VUE test centres or online proctored, valid for three years.

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Questions before you buy

What do I get when I buy the AWS MLA-C01 pack?

271 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

How quickly do I receive it?

Straight away. The full PDF and a questions-only copy are emailed to you the moment your payment goes through, and the same links are on your order page.

Is there a free sample?

Yes. Ten questions from this pack, with answers and explanations, are free on this page and as a PDF, so you can judge the quality before you pay.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

Can I share it with colleagues?

Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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