AWS AIF-C01 30-, 60-, and 90-day study plan for GenAI, RAG, Bedrock, review loops, and final-week priorities.
This page answers the question most candidates actually have: “How do I structure my AIF‑C01 prep?” Below are three realistic schedules (30/60/90 days) based on the official domain weights and the way AIF‑C01 questions are written (definitions + best-fit design choices + responsible use).
Use the plan that matches your available time, then follow the loop: Resources → drills → review misses → mixed sets → timed runs.
Most candidates land in a range based on background:
| Your starting point | Typical total study time | Best-fit timeline |
|---|---|---|
| You already work with AWS and have AI/GenAI basics | 25–40 hours | 30–60 days |
| You know AWS basics but are new to GenAI terms | 40–60 hours | 60 days |
| You’re new to both AWS and AI concepts | 60–80+ hours | 90 days |
Choose a plan based on hours per week:
| Time you can commit | Recommended plan | What it feels like |
|---|---|---|
| 8–12 hrs/week | 30‑day intensive | Fast learning + lots of practice |
| 4–7 hrs/week | 60‑day balanced | Steady progress + room for review |
| 3–4 hrs/week | 90‑day part‑time | Slow-and-solid with repetition |
AIF‑C01 domain weights:
| Domain | Weight | What you should be good at |
|---|---|---|
| Domain 1: Fundamentals of AI and ML | 20% | Core terminology, metrics, lifecycle, when ML is (and isn’t) a fit |
| Domain 2: Fundamentals of Generative AI | 24% | Tokens/embeddings/RAG basics, capabilities vs limitations, cost/latency trade-offs |
| Domain 3: Applications of Foundation Models | 28% | Prompting patterns, RAG design, evaluation, customization basics |
| Domain 4: Guidelines for Responsible AI | 14% | Fairness, transparency, safety, human oversight, documentation |
| Domain 5: Security, Compliance, and Governance for AI Solutions | 14% | Privacy, access control, auditability, governance basics |
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.
| If you are… | Use the plan like this |
|---|---|
| already comfortable with AWS but newer to AI | spend more time on terminology, RAG, FM applications, and responsible-AI judgment |
| comfortable with AI terms but newer to AWS services | spend more time on service fit, Bedrock roles, and security/governance controls |
| short on time | complete one pass of all five domains before chasing edge-case service detail |
| prone to memorizing definitions without applying them | turn every study block into a “which service or control fits this scenario?” drill |
| Minutes | What to do | Why |
|---|---|---|
| 0-10 | review one task area from the official exam guide | keeps the session anchored to real scope |
| 10-20 | restate the key definitions and pairwise contrasts aloud | prevents shallow recognition-only learning |
| 20-35 | do a short scenario drill or question set | checks whether you can apply the concept |
| 35-45 | write 2-3 miss rules and route the weakness to a domain | makes the next session targeted |
Target pace: ~8–12 hours/week. Goal: learn the vocabulary fast, then harden instincts through drills and mixed sets.
| Week | Focus (domains/tasks) | What to do | Links |
|---|---|---|---|
| 1 | Domain 1 fundamentals + start Domain 2 • Task 1.1 • Task 1.2 • Task 2.1 |
Build core vocabulary; make a one-page “terms” sheet. Do 2–3 focused drills and start a miss log. | Resources • Cheat Sheet • Glossary |
| 2 | Domain 1 lifecycle + Domain 2 limits + AWS services • Task 1.3 • Task 2.2 • Task 2.3 |
Learn “when gen AI is risky” + service pickers (Bedrock vs SageMaker vs pre-built AI services). End the week with a 30–40Q mixed set. | Cheat Sheet • Glossary |
| 3 | Domain 3 foundation model apps • Task 3.1 • Task 3.2 |
Build RAG + prompt instincts. Drill daily on prompt patterns, grounding, and safe tool use. | Resources • Glossary |
| 4 | Domain 3 evaluation/customization + Domains 4–5 + review • Task 3.3 • Task 3.4 • Task 4.1 • Task 4.2 • Task 5.1 • Task 5.2 |
Do 2 mixed sets + 1 timed run (65Q/90m). Review every miss and re-drill weak tasks until misses repeat less. | FAQ • Glossary |
Target pace: ~4–7 hours/week. Goal: more repetition and spaced review while steadily building practice volume.
| Weeks | Focus | What to do |
|---|---|---|
| 1–2 | Domain 1 + Task 2.1 | Build fundamentals; do 2 drills per week and keep a miss log. |
| 3–4 | Domain 2 (Tasks 2.2–2.3) | Focus on limitations, safety, and AWS service selection; end week 4 with a mixed set. |
| 5–7 | Domain 3 (Tasks 3.1–3.4) | RAG, prompting, evaluation, and customization basics; do weekly mixed sets. |
| 8 | Domains 4–5 + final review | 2 mixed sets + 2 timed runs; revisit weak tasks. |
Use task links from the Resources to drill each area as you go.
Target pace: ~3–4 hours/week. Goal: slow repetition with consistent drills and periodic mixed sets.
| Week | Focus (tasks) | What to do |
|---|---|---|
| 1 | Task 1.1 | Learn core terms; do one short drill set. |
| 2 | Task 1.2 | Service pickers by use case; write one-liner rules. |
| 3 | Task 1.3 | Lifecycle + MLOps vocabulary; do 1–2 drills. |
| 4 | Task 2.1 | Tokens/embeddings/RAG basics; do 1–2 drills. |
| 5 | Task 2.2 | Limits + risks; add to miss log. |
| 6 | Task 2.3 | Bedrock/SageMaker/service selection; do a mixed set. |
| 7 | Task 3.1 | RAG architecture + grounding; drill. |
| 8 | Task 3.2 | Prompt patterns + safety; drill. |
| 9 | Task 3.3 | Prompt vs RAG vs fine-tune; do 1–2 drills. |
| 10 | Task 3.4 | Evaluation rubric + safety checks; do a mixed set. |
| 11 | Task 4.1 + Task 4.2 | Responsible AI + explainability; drill. |
| 12 | Task 5.1 + Task 5.2 + final review | 1–2 mixed sets + 2 timed runs; re-drill weak tasks. |
Use timed practice to turn the reading layer into a repeatable review loop:
| Step | What to record |
|---|---|
| 1 | the weak domain: fundamentals, GenAI, FM applications, responsible AI, or governance/security |
| 2 | the real failure mode: term confusion, service fit, adaptation choice, safety control, or governance control |
| 3 | the one sentence rule you should have applied |
| 4 | the exact chapter or lesson to revisit next |
| Day | Focus |
|---|---|
| 7 | AI/ML Basics and GenAI Basics weak spots only |
| 6 | FM Apps with extra focus on RAG, prompting, and evaluation |
| 5 | Responsible AI and Governance |
| 4 | one mixed timed set and a miss log |
| 3 | re-drill only repeated miss patterns |
| 2 | one lighter timed run plus cheat-sheet review |
| 1 | glossary, short recall, and no heavy new topics |
| Weak domain | Fix approach |
|---|---|
| Fundamentals of AI and ML | rebuild the core vocabulary before doing more mixed sets |
| Fundamentals of GenAI | re-separate tokens, embeddings, context, RAG, and hallucination risk |
| Applications of Foundation Models | rework prompting, grounding, evaluation, and customization choices |
| Responsible AI | re-separate fairness, transparency, oversight, and safety controls |
| Security, Compliance, and Governance | re-separate access, encryption, privacy, auditability, and policy enforcement |
Before you sit the exam, you should be able to: