AWS Machine Learning Engineer Practice Questions (Free Sample)

Free practice sample

Fifteen original MLA-C01 practice questions, unlocked with no signup and instant scoring. The sample is spread across all four domains at blueprint proportions, so it reflects the shape of the real exam rather than a single topic slice.

MLA-C01 is written for engineers who ship and operate machine learning systems, not researchers who design architectures. Almost every question is a scenario, and the difficulty sits in service selection: SageMaker Feature Store versus Model Registry, Serverless Inference versus Asynchronous Inference, Clarify versus Model Monitor. Several options will be technically workable and one fits the constraint in the stem. Every explanation here names the winner and the near miss it beats.

Vendor · AWS Code · MLA-C01 Questions · 65 Duration · 130 min Pass · 720 / 1000 scaled Cost · $150 USD 4 domains Valid 3 years
Data Preparation for Machine Learning Domain 1 · 28%
ML Model Development Domain 2 · 26%
Deployment and Orchestration of ML Workflows Domain 3 · 22%
ML Solution Monitoring, Maintenance, and Security Domain 4 · 24%

AWS Machine Learning Engineer Associate Practice Questions

  1. Question 1 of 15Data Preparation for Machine Learning

    A training dataset for a fraud model contains 400,000 legitimate transactions and 900 fraudulent ones. A first model reaches 99.7 percent accuracy but catches almost no fraud. What is the most appropriate first step?

  2. Question 2 of 15Data Preparation for Machine Learning

    A team must join clickstream data landing continuously in Amazon S3 with customer records in Amazon RDS, then produce a feature table for training. They want a managed, largely visual ETL service. Which is the best fit?

  3. Question 3 of 15Data Preparation for Machine Learning

    Several teams keep recomputing the same customer aggregates for different models, and training and inference occasionally disagree on the values. Which SageMaker capability addresses this directly?

  4. Question 4 of 15Data Preparation for Machine Learning

    A dataset contains a categorical column with roughly 15,000 distinct values. Which encoding approach is most appropriate before training a gradient boosted tree model?

  5. Question 5 of 15Machine Learning Model Development

    A model reaches 0.97 accuracy on training data and 0.71 on the held-out validation set. What does this indicate and what is the appropriate response?

  6. Question 6 of 15Machine Learning Model Development

    Which SageMaker feature automatically searches hyperparameter combinations to optimise a chosen objective metric?

  7. Question 7 of 15Machine Learning Model Development

    A regulated lender must explain which input features drove each individual credit decision. Which service addresses this requirement?

  8. Question 8 of 15Machine Learning Model Development

    A team wants to reduce training cost for a model that tolerates interruption and can checkpoint progress. Which option offers the largest saving?

  9. Question 9 of 15Deployment and Orchestration of ML Workflows

    A model receives a few hundred requests per day in unpredictable bursts, and the team wants to avoid paying for idle capacity. Which SageMaker inference option fits best?

  10. Question 10 of 15Deployment and Orchestration of ML Workflows

    A team wants to release a new model version to a small share of live traffic, compare it against the current model, and roll back quickly if metrics degrade. Which approach fits?

  11. Question 11 of 15Deployment and Orchestration of ML Workflows

    Which service is designed to define and run a repeatable multi-step machine learning workflow covering processing, training, evaluation, and conditional registration?

  12. Question 12 of 15ML Solution Monitoring, Maintenance, and Security

    Six months after deployment a model shows steadily declining precision although the code has not changed. Which capability detects the most likely cause?

  13. Question 13 of 15ML Solution Monitoring, Maintenance, and Security

    A SageMaker training job must read from a specific S3 bucket and write model artefacts back, and nothing more. What is the correct approach?

  14. Question 14 of 15ML Solution Monitoring, Maintenance, and Security

    Which requirement is best met by enabling SageMaker Model Registry in a machine learning workflow?

  15. Question 15 of 15ML Solution Monitoring, Maintenance, and Security

    Training data contains customer records subject to privacy requirements, and the team must ensure the data is encrypted at rest with a key they control and can audit. Which approach is appropriate?

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Frequently asked questions about MLA-C01

How many questions are on the actual MLA-C01 exam?

65 questions in 130 minutes. Most are scenario-based rather than trivia, describing a real ML engineering situation and asking which approach fits best. Confirm current logistics at aws.amazon.com/certification when you book.

What score do I need to pass MLA-C01?

AWS reports a scaled score with 720 out of 1000 as the passing mark. That does not map linearly to a raw percentage. The Certifym practice pass mark is 72% raw as an honest equivalent, and it is a genuinely tight cut, you cannot coast on the easy domains and get lucky in the heavy ones.

Which domain should I spend the most time on?

Data Preparation, at 28%. It is the largest domain and the one candidates most often underestimate because they study modeling first. Model Development follows at 26%, then Monitoring, Maintenance and Security at 24%, with Deployment and Orchestration lightest at 22%, a fair map of where SageMaker engineers actually spend their time.

What does the exam cost and how long is it valid?

$150 USD, and the credential is valid for three years.

What experience should I have before sitting it?

MLA-C01 is the natural next credential after AWS Cloud Practitioner or Solutions Architect Associate for an engineer moving into MLOps. It assumes you already do the work it describes (preparing data, training and versioning models, deploying them, and operating them under real traffic), rather than studying modeling theory in the abstract.

How does MLA-C01 differ from the AI Practitioner certification?

AIF-C01 is conceptual and vendor-agnostic in tone; MLA-C01 is its practical counterpart and expects hands-on build work. It also differs from the older Machine Learning Specialty, which leans deeper into modeling theory. MLA-C01 is aimed squarely at the engineer shipping and operating ML systems.

How is this free sample different from the full Certifym bank?

The sample is a fixed 15-question set spread across the four domains at blueprint proportions. Each full Certifym practice exam is a 65-question set weighted to the blueprint at 18 / 17 / 14 / 16 across the domains, timed at 130 minutes with a 72% pass mark. None of the paid-bank items appear in this sample.

Is Certifym affiliated with AWS?

No. Certifym.net is operated by Certifym Exam Services, LLC and is not affiliated with, endorsed by, or sponsored by Amazon Web Services, Inc. All questions and explanations on this site are original content produced by Certifym and are not sourced from actual AWS exam questions.

Trademark notice & independence. Certifym.net is operated by Certifym Exam Services, LLC and is not affiliated with, endorsed by, or sponsored by Amazon Web Services, Inc. AWS, Amazon Web Services, and SageMaker are trademarks of Amazon.com, Inc. or its affiliates, used here only to identify the certification these study materials are intended for. The MLA-C01 exam domains and blueprint weightings are the property of AWS; download the current official exam guide at aws.amazon.com/certification.

All practice questions, answers, and explanations on this page are original content produced by Certifym Exam Services, LLC. They are not actual AWS examination questions and are not represented as such. Exam format, domain weights, cost, and validity are set by AWS and may change; verify current details before scheduling.