AWS AI Practitioner (AIF-C01): The Complete 2025 Study Guide
Everything you need to pass the AWS Certified AI Practitioner (AIF-C01): exam domains, key topics, AWS AI services cheat sheet, 6-week study plan, and service-selection scenarios.
AI Certification Guide
AWS AI Practitioner (AIF-C01): The Complete 2025 Study Guide
The AWS Certified AI Practitioner (AIF-C01) is AWS's newest foundational certification, launched in 2024. It validates your understanding of AI, machine learning, and generative AI concepts on AWS — and it's rapidly becoming the entry point for anyone who wants to build a career in AI.
This guide covers everything you need to know: exam structure, key topics, study plan, and strategies to pass on your first attempt.
Why the AIF-C01 Matters in 2025
AI and generative AI are no longer optional knowledge for cloud professionals. Every major enterprise is deploying AI-powered applications, and AWS wants to certify the professionals building and managing them. The AIF-C01 is specifically designed to:
- Validate foundational understanding of ML concepts without requiring coding skills
- Prove knowledge of AWS AI services: SageMaker, Bedrock, Rekognition, Comprehend, and more
- Certify responsible AI practices — bias, fairness, explainability, governance
- Serve as the foundation for the AWS ML Engineer Associate (MLA-C01) and GenAI Developer (AIP-C01)
Exam Overview
- Questions: 85 (multiple choice and multiple response)
- Duration: 120 minutes
- Passing score: 700 / 1000 (scaled)
- Cost: $100 USD
- Level: Foundational — no prerequisites required
- Recommended experience: 6+ months of exposure to AI/ML concepts
Exam Domains Breakdown
| Domain | Weight |
|---|---|
| Domain 1: Fundamentals of AI and ML | 20% |
| Domain 2: Fundamentals of Generative AI | 24% |
| Domain 3: Applications of Foundation Models | 28% |
| Domain 4: Guidelines for Responsible AI | 14% |
| Domain 5: Security, Compliance, and Governance for AI Solutions | 14% |
Key Topics by Domain
Domain 1 & 2: AI/ML and Generative AI Fundamentals
This is the conceptual foundation — no coding required, but you need to understand the terminology:
- ML model types: Supervised (regression, classification), unsupervised (clustering, anomaly detection), reinforcement learning
- Key ML concepts: Training/validation/test splits, overfitting vs. underfitting, bias-variance tradeoff, feature engineering
- Neural networks: Basic architecture, activation functions, backpropagation concept
- Transformer architecture: Attention mechanism, why transformers power LLMs
- Foundation models: What they are, how they differ from traditional ML, fine-tuning vs. RAG vs. prompt engineering
- Generative AI outputs: Tokens, embeddings, hallucinations, temperature, top-p sampling
Domain 3: Applications of Foundation Models (The Most Tested Domain)
This domain focuses heavily on Amazon Bedrock — AWS's managed service for foundation models:
- Amazon Bedrock: Available models (Anthropic Claude, Meta Llama, Amazon Titan, Mistral, Cohere), how to invoke models via API, model evaluation
- Prompt engineering: Zero-shot, few-shot, chain-of-thought prompting, system prompts, prompt injection risks
- RAG (Retrieval Augmented Generation): Vector stores, Knowledge Bases for Amazon Bedrock, when to use RAG vs. fine-tuning
- Bedrock Agents: What they are, action groups, tool use, orchestration
- Bedrock Guardrails: Content filtering, topic denial, PII detection, grounding
Domain 4 & 5: Responsible AI and Security
- Bias and fairness: Types of bias (data bias, model bias, sampling bias), mitigation strategies
- Explainability: SHAP values concept, model cards, SageMaker Clarify
- AI governance: AWS AI Service Cards, model evaluation, human review (A2I)
- Security: IAM for Bedrock, VPC endpoints, encryption of model inputs/outputs, prompt injection
AWS AI Services You Must Know
Beyond Bedrock, the exam tests knowledge of AWS's purpose-built AI services:
- Amazon SageMaker: Training jobs, endpoints, Studio, Pipelines, Feature Store, Model Monitor
- Amazon Rekognition: Image/video analysis, facial recognition, content moderation
- Amazon Comprehend: NLP — entity recognition, sentiment analysis, key phrase extraction
- Amazon Textract: Document extraction (forms, tables) — vs. Comprehend (plain text NLP)
- Amazon Transcribe: Speech-to-text, custom vocabulary, speaker identification
- Amazon Polly: Text-to-speech, SSML, neural TTS
- Amazon Lex: Chatbots, intents, slots, fulfillment
- Amazon Kendra: Intelligent enterprise search with document understanding
- Amazon Personalize: Recommendation systems, recipe-based approach
- Amazon Forecast: Time-series forecasting, AutoPredictor
6-Week Study Plan
Weeks 1–2: Foundations
- Read AWS's official AIF-C01 exam guide (free on aws.amazon.com/training)
- Study ML fundamentals: model types, training pipeline, evaluation metrics (accuracy, precision, recall, F1)
- Understand the generative AI concepts: LLMs, transformers, tokens, embeddings
- Take 30 practice questions per day focused on Domains 1 & 2
Weeks 3–4: AWS Services Deep Dive
- Hands-on: Create a free AWS account and explore Amazon Bedrock (free tier available)
- Study each AI service: Rekognition, Comprehend, Textract, Transcribe, Polly, Lex, Kendra
- Learn Amazon SageMaker concepts (no need to build models — understand the workflow)
- Practice 50 questions per day on Domain 3 (Applications of Foundation Models)
Weeks 5–6: Responsible AI, Security & Practice Exams
- Study AWS's responsible AI principles and security for AI services
- Take 2–3 full-length 85-question practice exams
- Review all incorrect answers — focus on why the correct answer is correct
- Target 80%+ on practice exams before your real exam day
Key Differentiators to Memorize
The exam loves testing your ability to pick the right AWS service for a scenario:
| If the scenario says... | Use this service |
|---|---|
| "Identify objects/people in images" | Amazon Rekognition |
| "Extract text from scanned documents/forms" | Amazon Textract |
| "Analyze sentiment in customer reviews" | Amazon Comprehend |
| "Build a chatbot for customer service" | Amazon Lex |
| "Convert call center recordings to text" | Amazon Transcribe |
| "Use a foundation model like Claude or Llama" | Amazon Bedrock |
| "Augment LLM with company documents/knowledge base" | Bedrock Knowledge Bases (RAG) |
"The AIF-C01 is more about understanding when to use which AWS AI service than deep technical implementation. Focus on use-case scenarios and you'll do well."
After AIF-C01: Your AI Career Path
The AIF-C01 is your entry point to the AWS AI certification ladder:
- AWS ML Engineer Associate (MLA-C01): Hands-on ML engineering, SageMaker pipelines, MLOps
- Anthropic Claude Certified Architect: Master Claude API, prompt engineering, production LLM architecture
- AWS GenAI Developer Professional (AIP-C01): Enterprise generative AI architecture with Bedrock
CertLand covers all these certifications in one AI career path — start with the AIF-C01 practice exams and build from there.