AWS Machine Learning Engineer Associate (MLA-C01) Practice

How the MLA-C01 exam is structured

AWS Machine Learning Engineer - Associate is a AWS certification with approximately 65 questions in 130 minutes. The table below shows the exam weighting published for each domain, so you can decide where to spend your study time instead of working through the syllabus in order.

Domain	Exam weight	What it covers
Data Preparation for Machine Learning (ML)	28%	Ingest, transform, and prepare data for machine learning workflows
ML Model Development	26%	Select algorithms, train models, and optimize performance
ML Solution Monitoring, Maintenance, and Security	24%	Monitor ML systems, detect drift, and secure ML workflows
Deployment and Orchestration of ML Workflows	22%	Deploy models and orchestrate end-to-end ML workflows

A passing study plan usually trails the weighting: spend the most sessions on Data Preparation for Machine Learning (ML) (28%), then check the next domain down before moving on. If your accuracy on a high-weight domain stays under 80%, more practice there is worth more than another pass at a low-weight topic.

Worked sample MLA-C01 questions

These questions come from the AWS Machine Learning Engineer - Associate practice bank. Read the rationale even when you answer correctly — the explanation is usually where the exam technique lives.

Sample question 1 — ML Solution Monitoring, Maintenance, and Security

A recommendation team has trained a new ranking model and wants to learn how it performs against the model currently serving live traffic. Leaders do not want to switch all users at once, and they want the comparison based on real requests rather than historical logs. The team needs a way to expose a small slice of production traffic to the new model while the current one keeps serving everyone else. Which approach should the team use?

A. Replay last month's request logs through the new model and compare the offline scores.
B. Add a second production variant to the endpoint and give it a small share of live requests.
C. Replace the current model with the new one and monitor the click-through rate for a week.
D. Duplicate the endpoint in a staging account and run synthetic traffic against both models.

Answer: B. Option B is correct because adding a second variant to the live endpoint and routing a small share of requests to it yields a real-world comparison without exposing the whole audience to an unproven ranker. Replaying last month's logs, option A, cannot show how the new ranking alters what users actually see. Wholesale replacement, option C, discards the baseline the comparison needs and risks the experience. Mirroring the endpoint in a separate account, option D, exercises synthetic rather than genuine production traffic.

Sample question 2 — Data Preparation for Machine Learning (ML)

A logistics provider runs several model workflows and wants every stored model file protected with a key it controls, while a compliance team needs to see, per access, which workload used that key. The provider also insists that no workflow outside the approved set be able to decrypt the stored models. Which AWS solution should the provider choose?

A. Apply a bucket policy that permits only the approved workflow roles to read the stored models.
B. Keep the model files inside a private subnet that has no route to the public internet.
C. Use the storage service's built-in encryption and review the access logs each month.
D. Encrypt Amazon S3 artifacts with AWS KMS customer managed keys and grant usage through scoped key policies.

Answer: D. Option D is correct because the provider-owned key in AWS KMS keeps control with the logistics business, the scoped key policies confine decryption to the approved workflows, and the key usage log answers the compliance team's per-access question. Option A would limit which roles may read the models but would not give the provider its own key or record key use. Option B would hide the files from the network without protecting them at rest. Option C would rely on provider-held encryption and would not tie each decryption to a specific workload.

Sample question 3 — Data Preparation for Machine Learning (ML)

An energy utility watches a demand-forecasting endpoint whose incoming request records occasionally arrive with missing sensor fields. Operators want to be warned as soon as the share of complete records in the live feed drops under the level that was measured when the service launched, so they can intervene before forecasts degrade. The existing logging already retains the inbound records. Which AWS setup alerts the utility at that moment?

A. Configure Amazon SageMaker Model Monitor data quality monitoring and alarm on the relevant CloudWatch metric.
B. Retrain the forecasting model nightly on the most recent records and compare the accuracy against the launch snapshot.
C. Reject any request that is missing a sensor field and return an error to the calling application.
D. Archive the inbound records to a second bucket and review the completeness figures with the operations team.

Answer: A. Option A is correct because the data quality monitor compares each live batch against the baseline statistics captured at launch and the CloudWatch alarm fires when completeness slips below that baseline, so operators hear about the drift the moment it begins. Option B would retrain on whatever arrived and would not warn anyone about missing fields. Option C would push the failure back to callers instead of telling the operators. Option D would store the records but would not raise any automatic notice when completeness drops.

How to use this MLA-C01 practice set

Answer the 10 free questions without notes. Guessing is more useful than looking the answer up — the wrong answers tell you which domain to revisit.

Read every explanation, including the ones you got right. Note the reasoning for each distractor, not just the correct option.

Drill by domain, starting with Data Preparation for Machine Learning (ML) (28%), then take a mixed set so you practise switching between topics.

Finish with a timed practice exam. Aim to be consistently above the passing score before you book the real MLA-C01 exam.

MLA-C01 practice questions FAQ

How many free MLA-C01 practice questions can I try?

You can answer 10 MLA-C01 practice questions free without creating an account. Each one is graded instantly with a written explanation. A free account keeps your progress, and the full bank (300+ questions) unlocks with a subscription.

What topics does the MLA-C01 exam cover?

The MLA-C01 exam is organised into 4 domains: Data Preparation for Machine Learning (ML) (28%), ML Model Development (26%), ML Solution Monitoring, Maintenance, and Security (24%), Deployment and Orchestration of ML Workflows (22%). The weighting above shows how much of the exam each domain represents, so budget study time in the same proportion.

How long is the MLA-C01 exam and how is it scored?

The MLA-C01 exam has approximately 65 questions in 130 minutes, with a passing score of 720. Practice sets here are scored the same way as the real exam so you can see whether you are above or below the line before test day.

How should I start studying for AWS Machine Learning Engineer - Associate?

Start with Data Preparation for Machine Learning (ML), the highest-weighted domain, then work down the list. Answer a mixed set after each domain pass — mixing domains forces you to identify what a question is asking before recalling the content, which is what the real exam does.

Are these the same questions that appear on the real MLA-C01 exam?

No. These are original practice questions written against the published MLA-C01 exam objectives. They mirror the style, difficulty, and domain weighting of the real exam, but the actual exam content is confidential and is not reproduced here.

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