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.
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.
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 |
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.
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 |
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.
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. |
Use the app to turn the syllabus into a repeatable loop:
Direct practice link: /app/cloud/#/topic-selection/aws_mla-c01