Exam prep for
AWS Certified Machine Learning Engineer – Associate
Exam set and awarded by Amazon Web Services On demand MLA-C02
For ML engineers preparing for the AWS Certified Machine Learning Engineer – Associate credential: strengthen working-level skills in preparing data, developing and deploying models, and operating secure ML and generative AI solutions on AWS.
Included with Elandi Pro: practice on all 4 exam domains and unlimited mock exams.
Independent exam prep. Elandi is not affiliated with, endorsed by or approved by Amazon Web Services, and does not award Amazon Web Services credentials. Our practice questions and mocks are written by Elandi, not taken from the real exam. Exam names are trademarks of their owners. AWS, Amazon Web Services and all related certification names are trademarks of Amazon.com, Inc. or its affiliates.
The exam in numbers
Assesses building, operationalizing, deploying and maintaining AI and machine learning solutions and pipelines on AWS, including traditional and foundation models. Questions use multiple-choice and multiple-response formats. Your mock exams use this learning plan's question count, time limit and pass mark.
- Questions
- 65
- Time limit
- 130min
- Mock pass mark
- 72%
- 47 correct
- Sitting
- On demand
- Single select (Radio): 4 options
- Multiple select (Checkbox): 5 options
Upcoming sittings
Pick a sitting when you start. Your workspace counts down to it and paces your prep from there.
- Any day On demand Book any day
What AWS Certified Machine Learning Engineer – Associate covers
4 modules · 12 units · ~9h of structured prep. Each exam domain with the weight the exam gives it, and the modules that prepare you for it.
- 01Data Preparation for ML and AI 28%
- Collect and store data
- Perform data transformation, feature engineering, and pre-processing
- Validate data quality and manage bias
- 02ML Model and Foundation Model (FM) Development 24%
- Choose appropriate modeling approaches for ML and AI solutions
- Train, fine-tune, and customize models for ML and AI solutions
- Analyze and evaluate the performance of ML and AI systems
- 03Deployment and Orchestration of ML and AI Workflows 24%
- Manage deployment infrastructure for ML and AI model types
- Provision and configure resources for ML and AI workloads based on existing architecture and requirements
- Implement automated orchestration and CI/CD pipelines for MLOps and AI workloads
- 04Operating, Monitoring, and Securing ML and AI Solutions 24%
- Monitor ML and AI model inference and performance
- Optimize and manage ML and AI infrastructure costs and performance
- Secure ML and AI workloads and model endpoints
Your exam workspace counts down and keeps score
Set your exam day once. The workspace paces your prep in four phases, weighs your coverage by what each topic is worth, and shows where the marks are still open after every mock.
- Overview
- All attempts
- Review
- Preview questions
- Questions
- 65
- Time limit
- 130 minutes
- Pass score
- 72% · 47 correct
days until your exam
6 weeks
Thursday, 24 December 2026
- Learn 8+ weeks out
- Mock and repair 8 to 2 weeks out
- Simulate Last 2 weeks
- Consolidate Last 3 days
Now: Mock and repair. Sit a full mock every week or two, then practise the topics you missed.
How ready you are
- Exam-weighted coverage
- 79% of the plan, counted by what each topic is worth
- Full mocks taken
- 2 Prep guides suggest three to five before the exam
- Latest mock
- 68% 4 points below the 72% pass mark
Where the marks are still open
- Operating, Monitoring, and Securing ML and AI Solutions 24% of the exam 54%
- Deployment and Orchestration of ML and AI Workflows 24% of the exam 71%
- Data Preparation for ML and AI 28% of the exam 88%
- ML Model and Foundation Model (FM) Development 24% of the exam 100%
How the content is written
Detailed, scenario-led associate-level exam preparation for experienced ML engineers, connecting AWS choices to constraints, evidence and operational consequences. Favor worked decisions over broad introductions while keeping code, mathematics and diagrams minimal.
- Worked examples
- Heavy
- Technical jargon
- Some
- Maths & formulas
- Light
- Code
- Light
- Diagrams
- Light
Try a sample question
Elandi wrote these in this learning plan's style. Pick an answer to see whether it's right and why. They are not taken from the exam.
A company needs generated answers grounded in internal policies that change often. It must update the knowledge available to the model without retraining and without placing the entire policy collection in every prompt. Which approach best fits? Choose ONE. One answer
A team has a trained SageMaker AI model for interactive predictions. Traffic is steady, requests require consistently low latency, and the team can keep provisioned capacity running. Which deployment option best fits? Choose ONE. One answer
A production model is returning less reliable predictions after input patterns change. Ground-truth labels arrive later. Before deciding whether to retrain, the engineer needs direct evidence of both data drift and predictive performance. Which TWO actions should the engineer take? Select all that apply
A deployed ML workload reads specific model artifacts from Amazon S3 using an IAM role. The security team needs least-privilege access and an audit record of individual object reads. Which TWO actions meet these requirements? Select all that apply
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