AIF-C01 — AWS Certified AI Practitioner Study Plan
A practical 7, 14, 30, and 60/90-day study plan for AWS Certified AI Practitioner (AIF-C01) candidates.
Study Plan orientation
This Study Plan is for candidates preparing for the real AWS Certified AI Practitioner (AIF-C01) exam from AWS. It is designed for people who need a practical schedule, not a general AI reading list.
The AIF-C01 exam is a foundational AI and generative AI certification. Your preparation should focus on:
- AI, machine learning, and generative AI concepts
- AWS AI and ML service selection
- Amazon Bedrock and foundation model concepts
- Prompt engineering and model response evaluation
- Responsible AI, security, privacy, and governance
- Basic cost, monitoring, and operational awareness
- Scenario-based decision-making under exam timing
Use the official AWS exam guide as your objective checklist. This page gives you the schedule and review rhythm to turn those objectives into daily work.
Which plan should you use?
| Time available | Best for | Study intensity | Main goal |
|---|---|---|---|
| 7 days | Final review, retake prep, or candidates with prior AWS/AI exposure | High | Identify weak areas, drill scenarios, and avoid late-stage overload |
| 14 days | Focused prep with some background in cloud, data, or AI concepts | Medium-high | Cover core concepts quickly and use practice to close gaps |
| 30 days | Balanced plan for most candidates | Medium | Build concepts, learn AWS service fit, and take multiple timed sets |
| 60/90 days | Newer candidates or those studying around work | Steady | Learn from the ground up, retain details, and mature exam judgment |
If you have 7 days and are starting from zero, prioritize diagnostic practice, AWS AI service selection, generative AI fundamentals, and responsible AI. Do not try to memorize every service detail.
Start with a diagnostic session
Before choosing daily topics, spend 60 to 90 minutes on a diagnostic review.
| Step | Action | Output |
|---|---|---|
| 1 | Skim the official AWS AIF-C01 exam guide | Objective checklist |
| 2 | Take 30 to 40 mixed practice questions without notes | Baseline score and timing |
| 3 | Tag every miss by topic | Weak-area map |
| 4 | Review explanations deeply | First review notes |
| 5 | Pick your plan length | Schedule commitment |
Use this simple tracker:
| Topic area | Confidence | Common misses | Next action |
|---|---|---|---|
| AI/ML fundamentals | Low / Med / High | Terms, model types, evaluation | Review concepts, then drill |
| Generative AI foundations | Low / Med / High | Foundation models, embeddings, RAG | Build scenario notes |
| Prompt engineering | Low / Med / High | Prompt structure, hallucination reduction | Practice prompt-choice questions |
| AWS AI/ML services | Low / Med / High | Wrong service selection | Make service-fit table |
| Security and governance | Low / Med / High | IAM, data protection, responsible AI | Review controls and scenarios |
| Monitoring, cost, and operations | Low / Med / High | Managed service tradeoffs | Drill architecture scenarios |
Daily practice rhythm
Use the same rhythm almost every study day. The topic changes; the structure stays consistent.
60-minute day
| Time | Activity | What to do |
|---|---|---|
| 10 min | Recall | Write what you remember from yesterday without notes |
| 20 min | Concept review | Study one focused topic from the exam guide |
| 20 min | Practice questions | Do 10 to 15 questions on that topic |
| 10 min | Miss review | Record why each missed answer was wrong |
90-minute day
| Time | Activity | What to do |
|---|---|---|
| 10 min | Warm-up review | Revisit missed-question notes |
| 30 min | Learn or review | Study one core concept or AWS service group |
| 30 min | Scenario practice | Answer mixed or topic-specific questions |
| 15 min | Error analysis | Rewrite weak concepts in your own words |
| 5 min | Plan tomorrow | Pick the next weakest topic |
2-hour day
| Time | Activity | What to do |
|---|---|---|
| 15 min | Recall | Summarize yesterday’s topic from memory |
| 35 min | Concept block | Review documentation, diagrams, or notes |
| 30 min | Hands-on concept review | Explore service use cases, console flow, or architecture examples |
| 25 min | Practice questions | Timed set of 15 to 25 questions |
| 15 min | Missed-question log | Update tags, fixes, and retest list |
AIF-C01 topic map
Use this map to organize review. Do not study services as isolated trivia. Study them as exam scenarios: “Which service, control, or approach best fits this requirement?”
| Study area | What to master | Practice action |
|---|---|---|
| AI and ML foundations | Difference between AI, ML, deep learning, supervised learning, unsupervised learning, classification, regression, training, inference, overfitting, evaluation | Explain each term in one sentence and identify it in scenarios |
| Generative AI foundations | Foundation models, large language models, embeddings, vector search, retrieval-augmented generation, fine-tuning concepts, model evaluation | Build a glossary and drill use-case questions |
| Prompt engineering | Clear instructions, context, constraints, examples, output format, hallucination reduction, prompt iteration | Rewrite weak prompts into stronger prompts |
| Amazon Bedrock concepts | Foundation model access, managed generative AI application patterns, guardrails, knowledge retrieval concepts, model selection tradeoffs | Practice “which capability fits?” scenarios |
| AWS AI services | Recognize common use cases for services such as text analysis, speech, translation, vision, document extraction, chatbots, search, and ML development | Create a service-to-use-case table |
| Responsible AI | Fairness, bias, transparency, explainability, human oversight, safety, privacy, and accountability | Identify the responsible AI risk in each scenario |
| Security and governance | IAM, least privilege, encryption concepts, data protection, monitoring, logging, access control, compliance-aware design | Drill “what is the safest option?” questions |
| Cost and operations | Managed service tradeoffs, right-sizing concept, monitoring usage, avoiding unnecessary custom builds | Compare managed AI service vs custom ML approach |
| Architecture judgment | Choosing the simplest AWS service or pattern that satisfies the requirement | Practice mixed scenario questions under time |
7-day final review plan
Use this if the exam is one week away. This is not a full beginner course. It is a final review and weak-area sprint.
| Day | Focus | Study actions |
|---|---|---|
| 1 | Diagnostic and triage | Take a mixed timed set. Tag misses by topic. Build your final-week weak-area list. |
| 2 | AI/ML and generative AI foundations | Review core terminology, model types, embeddings, RAG, foundation models, training vs inference, and evaluation concepts. |
| 3 | Prompt engineering and model behavior | Drill prompt-quality scenarios, hallucination reduction, output constraints, context use, and model selection tradeoffs. |
| 4 | AWS AI service selection | Make a one-page service-fit chart. Practice choosing the right AWS AI/ML service for business scenarios. |
| 5 | Security, governance, responsible AI | Review IAM, least privilege, data protection, monitoring, fairness, bias, privacy, and human oversight scenarios. |
| 6 | Timed mock and deep review | Take a timed mock or large mixed set. Spend at least as long reviewing as you spent answering. |
| 7 | Light final review | Review missed-question log, service-fit chart, and definitions. Do not start broad new topics. |
7-day rules
- Stop adding broad new material after Day 5.
- Use Day 6 for timed performance, not passive reading.
- Use Day 7 for recall, light review, and exam logistics.
- If a topic is still weak, learn the decision pattern, not every detail.
14-day focused plan
Use this if you have two weeks and can study most days.
| Day | Focus | Main work |
|---|---|---|
| 1 | Diagnostic | Mixed practice set, objective checklist, weak-area map |
| 2 | AI/ML basics | AI vs ML, supervised vs unsupervised, classification, regression, training, inference |
| 3 | Model evaluation | Accuracy concepts, overfitting, bias, model performance, business-fit decisions |
| 4 | Generative AI foundations | Foundation models, LLMs, embeddings, vector search, RAG |
| 5 | Prompt engineering | Prompt structure, examples, constraints, output formatting, hallucination mitigation |
| 6 | Amazon Bedrock concepts | Model selection, managed generative AI application patterns, guardrails, knowledge retrieval concepts |
| 7 | Review set 1 | Timed mixed set. Review every miss and update notes. |
| 8 | AWS AI services | Service use cases for language, vision, speech, documents, translation, search, and chat |
| 9 | ML development services | Understand where managed AI services end and ML development platforms begin |
| 10 | Security and privacy | IAM, least privilege, encryption concepts, data handling, monitoring, logging |
| 11 | Responsible AI | Fairness, bias, explainability, safety, human review, governance |
| 12 | Cost and operations | Managed vs custom approaches, monitoring usage, operational simplicity |
| 13 | Timed mock | Take a full timed practice exam or large timed set. Review deeply. |
| 14 | Final review | Retest misses, review service chart, stop new material, prepare for exam timing |
14-day priority order
If you fall behind, protect these items first:
- AWS AI service selection
- Generative AI and Amazon Bedrock concepts
- Prompt engineering
- Responsible AI and security
- AI/ML foundations
- Cost and operations
30-day balanced plan
Use this if you want a complete but efficient preparation cycle.
Weekly structure
| Week | Focus | Outcome |
|---|---|---|
| Week 1 | Foundations and diagnostic | Understand core AI/ML/generative AI language |
| Week 2 | Generative AI, prompt engineering, Amazon Bedrock | Recognize generative AI patterns and risks |
| Week 3 | AWS services, security, governance, operations | Choose the right service and control in scenarios |
| Week 4 | Timed mocks and weak-area sprint | Improve speed, confidence, and exam judgment |
Week 1: foundations
| Day | Focus | Actions |
|---|---|---|
| 1 | Diagnostic | Take a mixed set and tag every miss |
| 2 | AI/ML basics | Review AI, ML, deep learning, training, inference |
| 3 | Learning types | Supervised, unsupervised, classification, regression |
| 4 | Evaluation concepts | Model quality, overfitting, bias, business objective alignment |
| 5 | Data concepts | Features, labels, datasets, data quality, privacy considerations |
| 6 | Practice drill | Topic questions on AI/ML basics |
| 7 | Review | Rewrite weak definitions from memory |
Week 2: generative AI and prompt engineering
| Day | Focus | Actions |
|---|---|---|
| 8 | Generative AI basics | Foundation models, LLMs, text/image generation concepts |
| 9 | Embeddings and retrieval | Embeddings, vector search, retrieval-augmented generation |
| 10 | Prompt engineering | Instructions, context, examples, constraints, output format |
| 11 | Model behavior | Hallucinations, safety, response evaluation, human review |
| 12 | Amazon Bedrock concepts | Managed generative AI application patterns and model selection |
| 13 | Practice drill | Generative AI and prompt scenarios |
| 14 | Timed set | Mixed timed set and deep review |
Week 3: AWS services, security, and responsible AI
| Day | Focus | Actions |
|---|---|---|
| 15 | AWS language and text services | Match text analysis, translation, search, and chatbot scenarios to services |
| 16 | Speech, vision, and document use cases | Match transcription, speech, image, video, and document extraction scenarios |
| 17 | ML development concepts | Know when a managed AI service is enough vs when ML development is needed |
| 18 | Security | IAM, least privilege, encryption concepts, access control, monitoring |
| 19 | Responsible AI | Bias, fairness, explainability, privacy, safety, governance |
| 20 | Cost and operations | Managed service tradeoffs, usage monitoring, operational simplicity |
| 21 | Practice drill | Mixed AWS service-selection questions |
Week 4: exam readiness
| Day | Focus | Actions |
|---|---|---|
| 22 | Mock 1 | Take a timed mock or large mixed set |
| 23 | Mock 1 review | Review misses, guessed answers, and slow questions |
| 24 | Weak area 1 | Focused review on your weakest topic |
| 25 | Weak area 2 | Focused review on your second weakest topic |
| 26 | Mock 2 | Take another timed mock or large mixed set |
| 27 | Mock 2 review | Build final one-page notes |
| 28 | Service-selection sprint | Drill scenarios until service fit feels automatic |
| 29 | Final mixed review | Light timed set, missed-question retest |
| 30 | Exam-eve review | No broad new material; review notes and rest |
60/90-day full preparation path
Use this path if you are newer to AWS, AI, or cloud-based services. The goal is durable understanding, not cramming.
60-day path
| Phase | Days | Focus | Deliverable |
|---|---|---|---|
| Phase 1 | 1-7 | Diagnostic and exam objective map | Baseline score, weak-area tracker |
| Phase 2 | 8-18 | AI/ML foundations | Clear notes on model types, training, inference, evaluation |
| Phase 3 | 19-30 | Generative AI foundations | Notes on FMs, LLMs, embeddings, RAG, prompt engineering |
| Phase 4 | 31-40 | AWS AI and ML service selection | Service-fit chart with common use cases |
| Phase 5 | 41-48 | Security, governance, responsible AI | Controls and risk checklist |
| Phase 6 | 49-55 | Timed mocks and review | Mock results, error trends, retest list |
| Phase 7 | 56-60 | Final review | Concise notes, light practice, exam readiness check |
90-day path
For 90 days, use the same phases but slow the pace:
| Phase | Suggested duration | How to use the extra time |
|---|---|---|
| Diagnostic and planning | 1 week | Take two smaller diagnostics and compare error patterns |
| AI/ML foundations | 3 weeks | Add more concept practice and explain terms aloud |
| Generative AI | 3 weeks | Spend more time on prompt engineering, RAG, and model behavior |
| AWS services | 3 weeks | Build a stronger service-selection chart with scenario examples |
| Security and responsible AI | 2 weeks | Drill governance, privacy, monitoring, and human oversight scenarios |
| Mock and final review | 1 to 2 weeks | Use timed sets, retest misses, and reduce careless errors |
Weekly rhythm for 60/90 days
| Day type | Activity |
|---|---|
| 3 days per week | Learn or review one objective area |
| 2 days per week | Topic-specific practice questions |
| 1 day per week | Mixed review or timed set |
| 1 day per week | Rest, light flashcards, or catch-up |
Hands-on concept review
The AWS Certified AI Practitioner (AIF-C01) exam is not a deep engineering lab exam, but hands-on familiarity can improve scenario judgment.
Use lightweight hands-on review where it helps:
| Concept | Practical review |
|---|---|
| Amazon Bedrock | Understand the managed generative AI workflow, model selection concept, prompt testing flow, and guardrail purpose |
| Prompt engineering | Compare vague prompts with prompts that include role, task, context, constraints, and output format |
| Service selection | Map business requests to AWS AI services rather than memorizing service names in isolation |
| Security | Review how IAM, least privilege, encryption concepts, and logging support AI workloads |
| Monitoring | Understand why usage, errors, performance, and outputs may need review |
| Responsible AI | Identify where human review, bias checks, transparency, and safety controls fit |
If you use an AWS account, avoid unnecessary spend. Focus on reading service pages, reviewing architecture examples, and understanding workflows rather than building complex projects.
Missed-question review method
A missed question is not finished when you read the explanation. It is finished when you can explain the decision pattern.
Use this log:
| Field | What to write |
|---|---|
| Question topic | Example: prompt engineering, responsible AI, Amazon Bedrock, IAM |
| Why I missed it | Misread, did not know term, confused services, guessed too fast |
| Correct rule | The principle that decides the answer |
| Why wrong options are wrong | One short reason for each tempting option |
| Retest date | When you will answer a similar question again |
Error categories to track
| Error type | Fix |
|---|---|
| Concept gap | Review the topic, then answer 10 focused questions |
| Service confusion | Add the service to your service-fit chart |
| Security oversight | Ask: who has access, how is data protected, how is activity monitored? |
| Responsible AI miss | Ask: bias, privacy, transparency, safety, or human oversight? |
| Prompting miss | Identify missing context, constraints, examples, or output format |
| Timing error | Practice smaller timed sets before another mock |
| Careless reading | Underline requirement words such as “most appropriate,” “least operational effort,” or “reduce risk” |
Timed mock exam strategy
Timed practice is where you test exam readiness. Do not use all mock exams too early.
| Plan | When to use timed mocks | Recommended use |
|---|---|---|
| 7 days | Day 1 and Day 6 if available | One diagnostic, one final readiness check |
| 14 days | Day 7 and Day 13 | Midpoint correction and final review |
| 30 days | Days 22 and 26, plus smaller timed sets | Build stamina and reduce repeated errors |
| 60/90 days | After core coverage, then weekly near the end | Track trends, not just scores |
After every timed mock:
- Review all missed questions.
- Review all guessed questions, even if correct.
- Review all slow questions.
- Sort misses by topic.
- Pick the top two weak areas for the next study block.
- Retake only after you have corrected the underlying issue.
As a private readiness benchmark, many candidates aim for consistently strong results on fresh practice sets before exam day. Do not treat any practice score as an official AWS passing standard.
Final-week rules
During the final week, your goal is precision and recall.
Do
- Review the official exam objectives.
- Retest missed questions.
- Rebuild your service-selection chart from memory.
- Practice scenario questions under time.
- Review responsible AI and security decision patterns.
- Sleep normally before the exam.
Do not
- Start broad new topics in the last 48 hours.
- Take a full mock late the night before the exam.
- Memorize answer letters from practice questions.
- Ignore questions you guessed correctly.
- Spend all your time rereading notes without answering questions.
Exam-readiness checks
You are likely ready when you can do most of the following:
| Readiness check | Yes / No |
|---|---|
| I can explain AI, ML, deep learning, generative AI, training, and inference clearly. | |
| I can identify common AWS AI service use cases from scenarios. | |
| I can recognize when Amazon Bedrock or a managed generative AI pattern is appropriate. | |
| I understand prompt engineering basics and how to reduce poor model outputs. | |
| I can identify responsible AI concerns such as bias, fairness, privacy, safety, and transparency. | |
| I can apply IAM, least privilege, encryption concepts, monitoring, and governance to AI scenarios. | |
| I can complete timed mixed sets without rushing at the end. | |
| My missed-question log shows fewer repeated errors. | |
| I can explain why wrong options are wrong, not just why the right option is right. |
If several checks are still “No,” spend your next study session on targeted review rather than another full mock.
Practical next step
Start with a timed diagnostic set, build your missed-question tracker, and choose the 7-day, 14-day, 30-day, or 60/90-day path that matches your calendar. Then follow the daily rhythm: learn one focused topic, answer exam-style questions, and review every miss until the decision pattern is clear.