AWS / Associate / MLA-C02
Machine Learning Engineer Associate
Take models from representative data to monitored production, including foundation models and agentic workloads.
MLA-C02 is the updated beta track announced for September 2026. English MLA-C01 testing ended 28 September 2026; some other languages retain C01 during the beta period. Confirm the booked language/version before using this guide.
THE REVISION PATH
Your topics, in order.
Read. Recall. Explain the alternative.
The ML Lifecycle and MLOps
Split before learning transformations; evaluate before deployment; monitor after deployment.
Ingestion, Streaming and Transformation
A durable checkpoint and an idempotent sink matter more than a promise of no retries.
Foundation Models and Generative AI
A model predicts plausible output; your application must establish whether it is useful and supported.
Prompt Engineering, RAG and Fine-Tuning
Prompt for instructions, retrieve for current evidence, tune for learned behaviour.
Agents, Tools and AWS AI Platforms
The model proposes; tools act; policy decides whether an action is allowed.
Evaluation and Responsible AI
Measure the answer, the experience and the harm; averages can hide who fails.
AI Security, Privacy and Governance
Protect the data path and the action path, then keep evidence of both.
Feature Engineering, Training and Experimentation
Prevent leakage, track experiments and optimise the metric that represents the real failure cost.
Model Deployment, Pipelines and Production Monitoring
A model artifact is only one component of a reliable prediction service.
How this guide is organised
Original revision notes arranged around practical decisions. The linked official objectives define the mapped scope; primary documentation supports the explanations. Read each topic, answer without looking, then explain why another option would fail.
- Official exam guide ↗ Scope authority
Revision material supports preparation; it does not guarantee every possible exam question. Check the exam version and official objectives before booking.