MLA-C01AWS Certified Machine Learning Engineer – Associate
What the exam covers
From AWS
About MLA-C01
Putting models into production with SageMaker: preparing features, training and tuning, choosing an inference option, building pipelines, and monitoring for drift. It is an engineering exam, so there is more about deployment and operations than about the maths.
Who it's for. ML engineers, MLOps engineers and data scientists who deploy their own models.
How to prepare
- Match inference options to traffic: real-time, serverless, asynchronous or batch transform.
- Know what SageMaker Model Monitor and Clarify each detect, and which baseline each one needs.
- Feature Store, Data Wrangler and Pipelines show up as the "least effort" answer surprisingly often.
Sample MLA-C01 questions
Try these with the answers on the page, then practise the full bank.
- A marketing analytics team has raw customer exports with inconsistent date formats and blank cells. They want a point-and-click workspace where analysts can apply hundreds of ready-made cleaning steps and save the sequence as a reusable recipe, without writing any scripts. Which AWS service fits? Data preparation for ML
- A computer vision startup needs thousands of images annotated with bounding boxes. It wants a managed labeling service that can draw on its own staff, a vendor team, or a public crowd, and that trains a model to auto-label the easy images so people focus on the hard ones. Which service should it use? Data preparation for ML
- A team wants a repeatable ML workflow that preprocesses data, trains a model, evaluates it, and registers the model only if its metrics pass a threshold. The whole flow should be defined as a directed graph and integrate natively with the model catalog. Which service is best? Deployment and orchestration of ML workflows
- An analyst wants to run ad hoc ANSI SQL over Parquet files stored in an Amazon S3 bucket. She wants to pay only for the data each query scans and does not want to provision or manage a cluster. Which AWS service should she use? Data preparation for ML
- A travel app wants its assistant to check live seat availability and then book a reservation by calling the airline's booking API, with the model deciding when each call is needed. Which Amazon Bedrock feature supports this? ML model development
- A data scientist training a convolutional classifier notices the model memorizes its training images. Which regularization method should be added so that, during each training step, a random subset of hidden units is silenced? ML model development
- A media company stores raw video footage whose retrieval frequency is unpredictable, sometimes daily and sometimes not for months. The team does not want to write lifecycle rules or monitor access patterns. Which S3 option best fits? Data preparation for ML
- A research lab must train a multi-billion-parameter language model across hundreds of accelerator instances for several weeks. Hardware failures must be detected and faulty nodes replaced automatically without restarting the whole run from scratch. Which SageMaker option fits? ML model development
- All 22 sample questions
Questions people ask
Is this MLA-C01 practice exam free?
Yes. All 212 questions, the timed mock exam and the explanations are free, and there's no account to create.
Are these real exam questions?
No. Real exam questions are confidential under the AWS candidate agreement. These are practice questions written to cover the same domains in the same scenario style, each with an explanation of why the answer is right.
How is the MLA-C01 mock exam scored?
Questions are drawn from every domain in proportion to the exam guide, and you get a raw percentage with a breakdown by domain. AWS uses scaled scoring, so treat 75% or more here as a sign you're ready rather than a predicted score.
Where is my progress saved?
In your browser, on this device. Nothing is sent to us apart from the answers you check, and clearing site data resets it.