MLA-C02 study guide · 2026

The 4 MLA-C02 domains, explained

4 domains and 12 official task statements. Data Preparation for ML and AI carries the most weight at 28%. Each guide explains what the domain covers, how its questions are framed and where candidates lose marks.

Start with Domain 1 See all domains
Questions
65 (50 scored, 15 unscored) per exam guide; 85 during beta
Time
170 minutes (beta); GA duration not yet published
Passing score
720 / 1000
Outline
MLA-C02 exam guide (in use from September 29, 2026; beta until GA, listed for January 14, 2027)

Exam blueprint

Bar widths follow the official weights. Weight ranges are drawn at their midpoint.

  1. 1 28%
  2. 2 24%
  3. 3 24%
  4. 4 24%
  1. 1 Data Preparation for ML and AI
  2. 2 ML Model and Foundation Model (FM) Development
  3. 3 Deployment and Orchestration of ML and AI Workflows
  4. 4 Operating, Monitoring, and Securing ML and AI Solutions

All 4 domains

12 official task statements 52–68 hrs suggested study

  1. Domain 01 28%

    Data Preparation for ML and AI

    Domain 1 is the largest MLA-C02 domain. It covers getting data into AWS, choosing formats and stores, engineering features, preparing documents and embeddings for RAG, and proving that training data is clean, balanced and safe to use.

    Inside this domain

    • Collect and store data
    • Perform data transformation, feature engineering, and pre-processing
    • Validate data quality and manage bias

    Watch for Firehose for analytics

    Task statements
    3
    Questions est.
    ~14
    Study time est.
    14–18 hrs
  2. Domain 02 24%

    ML Model and Foundation Model (FM) Development

    Domain 2 covers choosing between ML models, AWS AI services and Bedrock foundation models, then training, tuning, customizing and evaluating them with SageMaker AI, Bedrock evaluations and metrics such as ROUGE, BERTScore and LLM-as-a-judge.

    Inside this domain

    • 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

    Watch for Fine-tune for freshness

    Task statements
    3
    Questions est.
    ~12
    Study time est.
    14–18 hrs
  3. Domain 03 24%

    Deployment and Orchestration of ML and AI Workflows

    Domain 3 covers putting ML models, foundation models and agents into production on AWS: choosing inference options, provisioning endpoints and knowledge bases, scaling GPU workloads, and automating training, testing and release with SageMaker Pipelines and AWS CI/CD services.

    Inside this domain

    • 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 continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads

    Watch for Always real-time

    Task statements
    3
    Questions est.
    ~12
    Study time est.
    12–16 hrs
  4. Domain 04 24%

    Operating, Monitoring, and Securing ML and AI Solutions

    Domain 4 covers running ML and AI systems after launch: monitoring models, FMs and agents for drift and failures, controlling infrastructure and token costs, and securing pipelines, endpoints, data and credentials with IAM, VPCs, logging and Bedrock Guardrails.

    Inside this domain

    • 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

    Watch for CloudWatch shows drift

    Task statements
    3
    Questions est.
    ~12
    Study time est.
    12–16 hrs

Study the heaviest domains first

Marks follow weight. This order puts the most marks behind you first, across about 52–68 hrs of study.

  1. 1 Data Preparation for ML and AI 28% · 14–18 hrs
  2. 2 ML Model and Foundation Model (FM) Development 24% · 14–18 hrs
  3. 3 Deployment and Orchestration of ML and AI Workflows 24% · 12–16 hrs
  4. 4 Operating, Monitoring, and Securing ML and AI Solutions 24% · 12–16 hrs

Try one

Practice questionDomain 1: Data Preparation for ML and AI

A retailer builds a Bedrock knowledge base over 40,000 product manuals. Users say answers mix up product models, even though the right manual is indexed. Each manual was embedded as a single chunk with no metadata. Which change will most improve retrieval relevance?

One trap from every domain

The answer that looks right, and the clue that gives it away.

  • Domain 1

    Firehose for analytics

    Clue The scenario mentions aggregations, windows or joins in real time.

  • Domain 2

    Fine-tune for freshness

    Clue Content changes often or answers need sources.

  • Domain 3

    Always real-time

    Clue Words such as nightly, occasional or large file.

  • Domain 4

    CloudWatch shows drift

    Clue The endpoint has no errors but results worsen.

Other Artificial Intelligence & Machine Learning certifications

Study guides for exams that overlap with MLA-C02.

Practise what you read

MLA-C02 domains: common questions

How many domains are in the MLA-C02 exam?

MLA-C02 has 4 domains with 12 official task statements between them: Data Preparation for ML and AI, ML Model and Foundation Model (FM) Development, Deployment and Orchestration of ML and AI Workflows and Operating, Monitoring, and Securing ML and AI Solutions.

Which MLA-C02 domain carries the most weight?

Data Preparation for ML and AI is the heaviest at 28%, followed by ML Model and Foundation Model (FM) Development (24%). The lightest is Operating, Monitoring, and Securing ML and AI Solutions (24%). Weights come from Amazon Web Services's official outline.

What is the MLA-C02 exam format?

Questions: 65 (50 scored, 15 unscored) per exam guide; 85 during beta. Time allowed: 170 minutes (beta); GA duration not yet published. Passing score: 720 / 1000.

How long should I study for MLA-C02?

Our planning estimate is 52–68 hrs across all domains, split by weight. It is an editorial estimate, not an official figure, so adjust it to your experience and to how you score on practice questions.

Where should I start?

Start with Data Preparation for ML and AI, because it carries the most marks, then work down by weight. Use the checklist on each domain page to confirm you can do what the domain asks before moving on.

Which outline is this guide based on?

This guide follows MLA-C02 exam guide (in use from September 29, 2026; beta until GA, listed for January 14, 2027), published by Amazon Web Services. It was last reviewed in October 2026.

Domain names, weights and task statements come from Amazon Web Services's official outline (MLA-C02 exam guide (in use from September 29, 2026; beta until GA, listed for January 14, 2027)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.