Simulate the real AWS Certified Machine Learning Engineer Associate MLA-C01 exam with full-length timed sessions. Questions drawn proportionally from all 4 official blueprint domains — the same mix you'll face on test day.
Simulate real exam conditions
For the most realistic MLA-C01 simulation, start a 60 or 120-question session, put away all notes, set a timer matching the real exam duration (130 minutes), and commit to each answer before moving forward. This trains the time management and decision-making skills the real exam tests.
This free MLA-C01 mock exam uses the same question distribution as the real AWS Certified Machine Learning Engineer Associate MLA-C01 exam. Each session draws questions proportionally from all 4 official blueprint domains published by Amazon Web Services, so the topic mix you see accurately reflects what you'll face on test day.
MLA-C01 Domain Distribution
ML Model Development
Data Preparation for Machine Learning
Deployment and Orchestration of ML Workflows
ML Solution Monitoring, Maintenance, and Security
Every question is written by certified engineers against the 2026 MLA-C01 exam objectives. These are original practice questions — not dumps — so you build real understanding rather than memorising answers.
Both the mock exam and practice test use the same question bank. The difference is in how you use them — and when to use each during your MLA-C01 study plan.
Practice test — for learning
Use the MLA-C01 practice test when you are studying a domain. Answer questions, read every explanation immediately, and build understanding. Do 10–30 questions per domain per session. This is your primary study tool for the first 4 weeks.
Go to practice test →Mock exam — for simulation
Use the MLA-C01 mock exam in the final 1–2 weeks before your test date. Complete a 60 or 120-question session without stopping, manage your time, then review all results at the end. This builds exam-day stamina and surfaces final weak spots.
Start 120-question mock →Try these sample questions from the mock exam bank. Commit to an answer before revealing the explanation.
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
Select an answer to reveal the explanation
A data scientist is using SageMaker built-in XGBoost algorithm for a binary classification task. Which objective metric is MOST appropriate for SageMaker Automatic Model Tuning to maximize?
Select an answer to reveal the explanation
A team is training a large language model using SageMaker with multiple GPUs. They need to reduce training time by splitting the model across devices due to memory constraints. Which distributed training strategy should they use?
Select an answer to reveal the explanation
A data scientist is preparing a large dataset for training a machine learning model. The dataset contains missing values in several columns. Which approach is the MOST efficient for handling missing values in a large dataset using AWS services?
Select an answer to reveal the explanation
A company is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?
Select an answer to reveal the explanation
A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?
Select an answer to reveal the explanation
A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The dataset contains a column 'CustomerID' that is a unique identifier. What should the data scientist do with this column before training the model?
Select an answer to reveal the explanation
A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?
Select an answer to reveal the explanation
A team has 200 small ML models that need to be served via HTTPS endpoints. Each model is used infrequently, and the team wants to minimize hosting costs. Which SageMaker deployment approach is MOST cost-effective?
Select an answer to reveal the explanation
An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?
Select an answer to reveal the explanation
A machine learning engineer is monitoring a deployed model for data drift. The input features are a mix of categorical and numerical columns. The baseline is from the training data. Which SageMaker Model Monitor feature should they enable to detect changes in the distribution of each feature over time?
Select an answer to reveal the explanation
A team receives alerts that their SageMaker endpoint latency has increased significantly. They check CloudWatch metrics and see Invocations rising, but ModelLatency remains stable. Which metric should they investigate to find the source of the increased latency?
Select an answer to reveal the explanation
A data scientist wants to track the lineage of models, datasets, and training jobs in SageMaker. Which SageMaker feature should they use to capture these relationships as artifacts and actions?
Select an answer to reveal the explanation
Answer all 13 questions to see your domain score breakdown
Sitting the MLA-C01 under real exam conditions is a skill in itself. Candidates who underperform often do so not because of knowledge gaps, but because of poor time management or test anxiety. Use your final mock exam sessions to address both.
The MLA-C01 exam lasts 130 minutes. Do not spend more than 90 seconds on any single question on the first pass. Flag difficult ones and return to them after completing the rest.
On every question, immediately eliminate obviously wrong choices. Even if you are unsure between two options, narrowing to two doubles your odds. Most MLA-C01 distractors contain a subtle error — re-read the scenario constraint before committing to the answer that sounds most familiar.
Amazon Web Services writes many MLA-C01 questions as realistic scenarios. Read the final sentence first — it tells you what is being asked. Then re-read the scenario with the question in mind to avoid wasting time on irrelevant details.
The real MLA-C01 is a mental marathon lasting 130 minutes. In the week before your exam, complete at least two full timed mock sessions on separate days to build concentration stamina. If you cannot stay focused for 130 minutes in practice, you will struggle on exam day.
Questions
50
On the real exam
Time limit
130 min
2.6 min per question
Passing score
700/1000
Scaled scoring
The MLA-C01 uses scaled scoring — your raw percentage correct is converted to a score out of 1000. Consistently scoring above 80% on mock exams puts you well above the 700/1000 threshold, giving you a buffer for any unexpected question types on the real exam.
Yes. Courseiva provides free MLA-C01 mock exam questions across all official exam domains. The platform includes timed simulation, per-domain score breakdown, missed-question review, and readiness tracking. No account required — free forever, supported by advertising.
The practice test is optimised for learning: you see explanations after each question immediately. The mock exam is optimised for simulation: you answer all questions under time pressure and review at the end. Use practice tests for studying and mock exams for benchmarking.
Aim for consistent scores of 80% or above on full-length MLA-C01 mock exams before booking your test date. The official passing score of 700/1000 corresponds to roughly 72–75% correct answers, so an 80% buffer accounts for difficulty variation and question styles on the real exam.
Most candidates who pass MLA-C01 on their first attempt complete 3–5 full-length mock exams in the two weeks before their test. This is enough to identify final weak spots, build stamina, and verify readiness without over-stressing or running out of fresh questions.
No — all Courseiva questions are original, written by certified engineers against public Amazon Web Services exam blueprints. Exam dumps are memorised real exam questions shared illegally. Using dumps violates your Amazon Web Services certification agreement and can result in your certification being revoked. Our questions make you genuinely competent, not just test-day lucky.
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