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AWS MLA-C01 Study Plan (30 / 60 / 90 Days)

A practical MLA-C01 study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus by domain, suggested hours/week, and tips for using the Mastery Cloud practice app.

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This page answers the question most candidates actually have: “How do I structure my MLA‑C01 prep?”
Below are three realistic schedules (30/60/90 days) based on the official domain weights and the way MLA‑C01 questions are written (scenario + trade-offs + operational realism).

Use the plan that matches your available time, then follow the loop: Syllabus → drills → review misses → mixed sets → timed runs.

How long should you study?

Typical ranges based on background:

Your starting point Typical total study time Best-fit timeline
You deploy SageMaker models and pipelines already 40–60 hours 30–60 days
You know ML but are newer to AWS/SageMaker 60–90 hours 60–90 days
You’re new to ML engineering and MLOps 90–120+ hours 90 days

Choose a plan based on hours per week:

Time you can commit Recommended plan What it feels like
10–15 hrs/week 30‑day intensive Fast learning + lots of practice
6–9 hrs/week 60‑day balanced Steady progress + room for review
3–5 hrs/week 90‑day part‑time Slow-and-solid with repetition

Use the exam weights to allocate time

MLA‑C01 domain weights:

Domain Weight What you should be good at
Domain 1: Data Preparation for ML 28% Data ingest/ETL, feature engineering, data integrity and bias basics
Domain 2: ML Model Development 26% Model choice, training/tuning, evaluation and explainability
Domain 3: Deployment + Orchestration 22% Endpoint choices, IaC, CI/CD for ML workflows
Domain 4: Monitoring + Security 24% Drift/monitoring, infra + cost tuning, audit/security controls

If you want one rule: spend ~60% learning + 40% practice early, then invert it to ~30% learning + 70% practice in the final 1–2 weeks.

30-Day Intensive Plan

Target pace: ~10–15 hours/week.
Goal: cover the blueprint quickly, then harden instincts through drills and mixed sets.

Week Focus (domains/tasks) What to do Links
1 Domain 1 data ingest + transform
• Task 1.1
• Task 1.2		 Build strong “data-to-features” instincts (formats, ETL tools, feature store). Do 2–3 focused drills and start a miss log. Syllabus • Cheatsheet • Practice
2 Domain 1 integrity + Domain 2 model selection
• Task 1.3
• Task 2.1		 Focus on data quality, bias basics, and picking the right approach (built-in vs custom vs managed AI services). End with a 30–40Q mixed set. Cheatsheet • Practice
3 Domain 2 training/tuning + evaluation
• Task 2.2
• Task 2.3		 Drill hyperparameters, overfit/underfit, Clarify/Debugger/Model Registry concepts. Do daily drills and one mixed set. Syllabus • Practice
4 Domain 3 deployment/CI-CD + Domain 4 monitoring/security
• Task 3.1
• Task 3.2
• Task 3.3
• Task 4.1
• Task 4.2
• Task 4.3		 Do 2 mixed sets + 1 timed run (65Q/130m). Review every miss and re-drill weak tasks until misses repeat less. Practice • FAQ

60-Day Balanced Plan

Target pace: ~6–9 hours/week.
Goal: spaced repetition and deeper drills while steadily building practice volume.

Weeks Focus What to do
1–2 Domain 1 (Tasks 1.1–1.3) Data ingest/ETL/features + integrity/bias basics; do 2 drills per week.
3–4 Domain 2 (Tasks 2.1–2.3) Model selection + training/tuning + evaluation; end week 4 with a mixed set.
5–6 Domain 3 (Tasks 3.1–3.3) Endpoint choices, IaC, containers, CI/CD; do weekly mixed sets.
7–8 Domain 4 (Tasks 4.1–4.3) + final review Monitoring/drift + cost + security; 2 timed runs and re-drill weak tasks.

Use task links from the Syllabus to drill each area as you go.

90-Day Part-Time Plan

Target pace: ~3–5 hours/week.
Goal: slow repetition with consistent drills and periodic mixed sets.

Week Focus (tasks) What to do
1 Task 1.1 Data formats + storage pickers; do one drill set.
2 Task 1.2 ETL tools + feature engineering; drill.
3 Task 1.3 Data quality + bias basics; drill.
4 Task 2.1 Model selection + AI services vs custom; drill.
5 Task 2.2 Training/tuning + registry/versioning; drill.
6 Task 2.3 Metrics + Clarify/Debugger; do a mixed set.
7 Task 3.1 Endpoint types + targets; drill.
8 Task 3.2 IaC + scaling metrics; drill.
9 Task 3.3 CI/CD + orchestration; drill.
10 Task 4.1 Drift + Model Monitor; drill.
11 Task 4.2 CloudWatch/CloudTrail + cost tools; drill.
12 Task 4.3 + final review Security + compliance; 2 timed runs and re-drill weak tasks.

How to integrate the Mastery Cloud app

Use the app to turn the syllabus into a repeatable loop:

  1. Start in the Syllabus and open a task.
  2. Drill that task in Practice.
  3. After each set, write 3–5 “rules” from your misses (for example: endpoint type depends on traffic shape, drift needs baseline + monitoring).
  4. Re-run weak tasks 48–72 hours later (spaced repetition).

Direct practice link: /app/cloud/#/topic-selection/aws_mla-c01

Overview
Syllabus