Exams / MLA-C02
Free AWS Certified Machine Learning Engineer – Associate (MLA-C02) practice exam
MLA-C02 is in beta. The standard exam is available from January 14, 2027. These questions follow the beta exam guide. What changes in MLA-C02
Start free practice No sign-up. Or choose a timed exam or one domain.
Take the full 65-question practice exam free, with no sign-up and no email. The questions are written like the real exam: short scenarios where you pick the best answer. Every question is original and explains why each option is right or wrong.
- Real exam
- 65 questions
- Time
- 130 minutes
- Domains
- 4
- Free exam
- 65 questions, no sign-up
Last verified against current AWS documentation ·How we write our questions
New to MLA-C02? The study guide explains what each domain tests and how to pick between close answers, with notes on every topic.Read the study guide
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Free practice exam
More practice sets
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Get all practice setsAll practice sets: 5 sets, 325 new questions
Fresh questions not in the free set, each with an explanation for every option. One payment of $14.99, no subscription, with access for at least 12 months. Includes the randomized test and topic practice across every set, and your answers are saved so you can track your weak areas.
- Practice set 165 questions
- Practice set 265 questions
- Practice set 365 questions
- Practice set 465 questions
- Practice set 565 questions
Taking more than one exam?
Get the all-exams passAll AWS exams pass: $49.99
Every practice set for all 11 AWS exams: 3,790 questions. That's about 3 exams' price for all 11. Includes exams we add later. One payment, no subscription.
Randomized test
For practice set owners. A fresh mix every time, drawn from every set you own: 65 questions in 130 minutes, like the real exam.
By domain
Untimed practice on one domain, with the explanation after each question.
- Data Preparation for ML and AI28% exam guide weighting · 18 free questionsIn my sets: Data Preparation for ML and AI
- ML Model and Foundation Model (FM) Development24% exam guide weighting · 16 free questionsIn my sets: ML Model and Foundation Model (FM) Development
- Deployment and Orchestration of ML and AI Workflows24% exam guide weighting · 16 free questionsIn my sets: Deployment and Orchestration of ML and AI Workflows
- Operating, Monitoring, and Securing ML and AI Solutions24% exam guide weighting · 15 free questionsIn my sets: Operating, Monitoring, and Securing ML and AI Solutions
By topic
Narrow it down to one topic. Counts are for the free set; the practice sets have more questions on every topic.
Data Preparation for ML and AI
- Ingesting and storing ML data2 free Ingesting and storing ML data questions to read
- Streaming data for ML2 free Streaming data for ML questions to read
- Vector databases for AI1 free Vector databases for AI questions to read
- SageMaker Feature Store2 free SageMaker Feature Store questions to read
- Transforming data with Glue, EMR and Data Wrangler2 free Transforming data with Glue, EMR and Data Wrangler questions to read
- Feature engineering1 free Feature engineering questions to read
- Embeddings and document prep for RAG2 free Embeddings and document prep for RAG questions to read
- Masking and redacting data1 free Masking and redacting data questions to read
- Preparing data for fine-tuning and distillation1 free Preparing data for fine-tuning and distillation questions to read
- Data quality, labeling and cleaning1 free Data quality, labeling and cleaning questions to read
- Bias and class imbalance3 free Bias and class imbalance questions to read
ML Model and Foundation Model (FM) Development
- Choosing foundation models in Bedrock1 free Choosing foundation models in Bedrock questions to read
- Choosing ML models and algorithms1 free Choosing ML models and algorithms questions to read
- AWS AI services1 free AWS AI services questions to read
- RAG architecture and retrieval tuning2 free RAG architecture and retrieval tuning questions to read
- Training with SageMaker AI3 free Training with SageMaker AI questions to read
- Hyperparameter tuning2 free Hyperparameter tuning questions to read
- Distributed training and training time1 free Distributed training and training time questions to read
- Overfitting, underfitting and convergence1 free Overfitting, underfitting and convergence questions to read
- Prompt engineering and fine-tuning1 free Prompt engineering and fine-tuning questions to read
- Experiments and reproducibility1 free Experiments and reproducibility questions to read
- Evaluating ML and GenAI models1 free Evaluating ML and GenAI models questions to read
- Explaining model outputs1 free Explaining model outputs questions to read
Deployment and Orchestration of ML and AI Workflows
- Real-time, serverless, async and batch inference3 free Real-time, serverless, async and batch inference questions to read
- Multi-model and multi-container endpoints1 free Multi-model and multi-container endpoints questions to read
- Deploying foundation models2 free Deploying foundation models questions to read
- Agents and agentic workflows1 free Agents and agentic workflows questions to read
- Endpoint scaling and provisioning2 free Endpoint scaling and provisioning questions to read
- Containers and endpoints in a VPC1 free Containers and endpoints in a VPC questions to read
- Bedrock knowledge bases2 free Bedrock knowledge bases questions to read
- SageMaker Pipelines and CI/CD2 free SageMaker Pipelines and CI/CD questions to read
- Model and prompt versioning1 free Model and prompt versioning questions to read
- Deployment guardrails and rollback1 free Deployment guardrails and rollback questions to read
Operating, Monitoring, and Securing ML and AI Solutions
- Model and data drift monitoring3 free Model and data drift monitoring questions to read
- Monitoring FMs and agents2 free Monitoring FMs and agents questions to read
- Inference cost and capacity2 free Inference cost and capacity questions to read
- IAM and least privilege for ML2 free IAM and least privilege for ML questions to read
- Network isolation and encryption2 free Network isolation and encryption questions to read
- Guardrails and responsible AI3 free Guardrails and responsible AI questions to read
- Auditing and pipeline security1 free Auditing and pipeline security questions to read
Frequently asked questions
Is the MLA-C02 practice exam really free?
- Yes. The free full-length 65-question MLA-C02 practice exam needs no sign-up and no email, and you can take it as often as you like, timed or at your own pace.
How many questions and how much time does the real MLA-C02 exam have?
- The AWS Certified Machine Learning Engineer – Associate exam has 65 questions and a time limit of 130 minutes, across 4 exam domains. The timed practice mode follows that pace.
Are these real exam questions?
- No. Every question is original practice material written for ExamPark, and each explains why every option is right or wrong, with links to the AWS documentation. We don't use recalled or leaked exam content. How we write questions is explained on the "How we write questions" page.
Do I need an account?
- Not for the free practice exam: it works without one, and your progress is saved on your device. You sign in with your email, with no password, to buy practice sets and keep your progress on every device.
What do the paid MLA-C02 practice sets add?
- 5 more full-length practice sets, 325 questions that are not in the free exam, plus randomized tests drawn from every set, topic practice across all of them, and answers saved so your weak areas are tracked over time. It is one payment with no subscription.
How long do I keep access to the practice sets?
- Access lasts at least 12 months from purchase, and it is a one-off payment, so there is nothing to cancel.
How do I know if I'm ready for MLA-C02?
- Take the timed practice exam and look at your score by domain: the weakest domains show where to study next. ExamPark's readiness estimate uses your recent timed exams. It is our own estimate, not an official AWS score, and no practice exam can promise a pass.
Additional resources
The official exam guide, free courses and the AWS reading the questions draw on.
See study resources