AWS AI Practitioner (AIP-C01) 4-Week Study Plan for Busy Professionals

You have decided to earn the AWS Certified AI Practitioner (AIP-C01) certification, but you do not have six or eight weeks to prepare. Maybe you have a project deadline coming up. Maybe your employer is paying for the exam and the voucher expires soon. Or maybe you simply work better with tight deadlines.

This 4-week plan is designed for professionals who can dedicate 1.5 to 2 hours per day, five days a week, with some extra time on weekends. It is aggressive but achievable if you stay consistent. If you want a more detailed overview of the exam itself — domains, services, and scoring — read our complete AIP-C01 study guide first.

Before You Start

You need three things before day one:

1. A study resource — AWS Skill Builder has a free digital training course for the AI Practitioner exam. Supplement it with a video course from a provider like Stephane Maarek or Adrian Cantrill if you prefer visual learning.
2. Practice questions — StudyKits has 97 question sets specifically for the AIP-C01 exam. You will use these throughout the plan to test knowledge and identify gaps.
3. A note-taking system — Keep a running document or notebook where you write down key concepts, service mappings, and anything you get wrong on practice questions. This becomes your personal cheat sheet for final review.

Week 1: AI/ML Fundamentals and Generative AI Concepts

This week covers Domains 1 and 2, which together represent 44% of the exam.

Day 1: Machine Learning Basics

Study the three types of machine learning: supervised (classification and regression), unsupervised (clustering and dimensionality reduction), and reinforcement learning. Understand when to use each type. Know common algorithms at a conceptual level — you do not need to implement them, but you need to recognize which algorithm fits which problem.

Practice target: 1 question set on ML fundamentals in StudyKits.

Day 2: The ML Pipeline

Study the end-to-end ML workflow: data collection, data cleaning, feature engineering, model training, evaluation, hyperparameter tuning, and deployment. Understand key metrics: accuracy, precision, recall, F1 score, AUC-ROC, RMSE. Know the difference between overfitting and underfitting.

Practice target: 1 question set on ML pipeline and evaluation.

Day 3: Foundation Models and LLMs

Study what foundation models are, how they differ from task-specific models, and why transfer learning matters. Understand transformer architecture at a high level — attention mechanisms, tokenization, pre-training vs fine-tuning. Know the major model families available on AWS (Anthropic Claude, Meta Llama, Amazon Titan, Mistral).

Practice target: 1 question set on generative AI fundamentals.

Day 4: Prompt Engineering and RAG

Study prompt engineering techniques: zero-shot, few-shot, chain-of-thought, and system prompts. Understand Retrieval-Augmented Generation (RAG) — why it exists, how it reduces hallucinations, and its basic architecture (documents, embeddings, vector store, retrieval, generation). Study model customization: fine-tuning, continued pre-training, RLHF.

Practice target: 1 question set on prompt engineering and RAG.

Day 5: Week 1 Review

Review your notes from the week. Take 2 mixed question sets that cover both Domain 1 and Domain 2. For every question you miss, write down the correct answer and why. Update your cheat sheet.

Practice target: 2 mixed question sets. Aim for 70%+ accuracy.

Weekend (Optional): Catch Up or Go Deeper

If you scored below 70% on any topic, use Saturday to re-study that area. If you are on track, use the time to watch a video course module on generative AI concepts.

Week 2: AWS AI Services and Bedrock Deep Dive

This week covers Domain 3, the largest domain at 28% of the exam.

Day 6: Amazon Bedrock

This is the most important service on the exam. Study: how to access foundation models through Bedrock, model selection criteria (cost, latency, capability), Bedrock Agents, Bedrock Knowledge Bases, Bedrock Guardrails, and model evaluation. Understand the difference between on-demand and provisioned throughput.

Practice target: 2 question sets on Bedrock.

Day 7: Amazon SageMaker Essentials

You do not need SageMaker expertise for this exam, but you need to know the key features: SageMaker Studio, Canvas (no-code ML), JumpStart (pre-trained models), Autopilot (automated ML), Feature Store, and Model Registry. Understand when to use SageMaker vs Bedrock.

Practice target: 1 question set on SageMaker.

Day 8: NLP Services

Study Amazon Comprehend (sentiment, entities, key phrases), Lex (chatbots), Polly (text-to-speech), Transcribe (speech-to-text), and Translate. For each service, know the primary use case, input/output types, and any important features like custom vocabularies or content redaction.

Practice target: 1 question set on NLP services.

Day 9: Vision, Search, and Other AI Services

Study Amazon Rekognition (image/video analysis), Textract (document extraction), Kendra (intelligent search), Personalize (recommendations), Forecast, and Fraud Detector. Also study Amazon Q Business and Amazon Q Developer. Create a mapping table: problem to service.

Practice target: 1 question set on vision and other AI services.

Day 10: Week 2 Review

Take 2 mixed question sets covering all AWS AI services. Focus on scenario-based questions that require you to choose the right service for a given problem. Update your service mapping cheat sheet.

Practice target: 2 mixed question sets. Aim for 75%+ accuracy.

Week 3: Responsible AI, Security, and Governance

This week covers Domains 4 and 5, which together account for 28% of the exam.

Day 11: Responsible AI Fundamentals

Study fairness and bias in AI: types of bias (sampling bias, measurement bias, confirmation bias), bias detection methods, and mitigation strategies. Study explainability: why it matters, SHAP values, feature importance. Study hallucination: causes, detection, and mitigation through RAG and guardrails.

Practice target: 1 question set on responsible AI.

Day 12: AWS Responsible AI Tools

Study SageMaker Clarify (bias detection and explainability), SageMaker Model Monitor (monitoring model drift), and Amazon Bedrock Guardrails (content filtering, topic restrictions, PII redaction). Understand how these tools fit into the ML lifecycle.

Practice target: 1 question set on responsible AI tools.

Day 13: Security for AI Workloads

Study IAM policies for AI services, data encryption (at rest and in transit), VPC configurations for SageMaker and Bedrock, CloudTrail for auditing AI service usage, and Amazon Macie for sensitive data discovery. Know compliance considerations for AI workloads (HIPAA, SOC 2, GDPR).

Practice target: 1 question set on AI security.

Day 14: Governance and Cost Management

Study model versioning, model registries, approval workflows, and cost management for AI services. Understand how to control costs for Bedrock (token-based pricing, provisioned throughput) and SageMaker (instance selection, spot instances, auto-scaling endpoints).

Practice target: 1 question set on governance.

Day 15: Week 3 Review

Take 2 mixed question sets covering Domains 4 and 5. These domains are often where candidates lose easy points because they under-study them. Do not make that mistake.

Practice target: 2 mixed question sets. Aim for 75%+ accuracy.

Week 4: Full Review and Exam Simulation

This is your final push. Everything this week is about consolidation and confidence building.

Day 16: Full Practice Exam 1

Take a complete 65-question practice exam in StudyKits under timed conditions (120 minutes). No notes, no pausing, no looking things up. When you finish, review every single question — correct and incorrect. Write down every concept you were unsure about.

Target: Complete the exam and identify your 3 weakest topic areas.

Day 17: Targeted Review Session 1

Take your list of weak areas from the practice exam and study them intensively. If Bedrock questions tripped you up, go back to Day 6 material. If responsible AI was the problem, revisit Days 11-12. Do 2-3 targeted question sets in your weak areas.

Practice target: 2-3 targeted question sets. Aim for 80%+ on weak areas.

Day 18: Service Mapping and Scenario Practice

Review your service mapping cheat sheet. For every AWS AI service on the exam, you should be able to answer: what does it do, when would I use it, and what are its key features. Take 2 scenario-heavy question sets that require choosing between services.

Practice target: 2 question sets focused on service selection scenarios.

Day 19: Full Practice Exam 2

Take another full-length practice exam under timed conditions. Compare your score to your first attempt. You should see improvement, especially in your previously weak areas. If you are scoring 80% or higher, you are ready.

Target: 80%+ overall score.

Day 20: Final Review

Review your cheat sheet one last time. Focus on any remaining gaps. Do a light review — 1 question set maximum. Do not cram. Go to bed early.

Practice target: 1 light question set. Confidence building, not learning new material.

Daily Time Breakdown

Here is how to structure your daily 1.5-2 hour study session:

• 0:00-0:45 — Study new material (video course, documentation, or reading)
• 0:45-1:15 — Practice questions in StudyKits (1-2 question sets)
• 1:15-1:30 — Review wrong answers and update cheat sheet
• 1:30-2:00 — (Optional) Additional practice or re-read difficult concepts

Tips for Staying on Track

Do not skip practice days. The review days at the end of each week are not optional. They are where real learning happens because they force you to apply knowledge across topics.

Use dead time. Waiting in line, commuting, eating lunch — open StudyKits on your phone and do a quick set of 10 questions. These micro-sessions add up fast over four weeks.

Do not chase perfection in week 1. You will not understand everything on the first pass. That is normal. The plan is designed so you revisit concepts through practice questions later.

Track your numbers. Write down your practice exam scores. Seeing the trend go up is motivating. If scores plateau, it tells you exactly where to focus.

Four weeks is tight, but hundreds of candidates pass the AIP-C01 in this timeframe. The key is consistency, not marathon study sessions. Follow the plan, do your practice questions daily in StudyKits, and you will walk into the exam ready.