About the exam
AIF-C01 Exam structure
AWS Certified AI Practitioner AIF-C01 prep with 601 original practice questions, domain-weighted mocks, flashcards, and topic recovery.
Issuer and path
AWS Certified AI Practitioner Exam Prep is administered through Amazon Web Services. Check official resources before booking, retesting, or relying on a stale requirement.
Fundamentals of AI and ML
20 scored + 0 pretest
Foundational AI, machine learning, data, learning methods, use cases, managed AWS AI services, development lifecycle, MLOps, and evaluation metrics.
Fundamentals of GenAI
24 scored + 0 pretest
Generative AI concepts, foundation models, tokens, embeddings, context engineering, agentic AI, business value, limitations, and AWS GenAI infrastructure.
Applications of Foundation Models
28 scored + 0 pretest
Foundation model selection, RAG, agents, vector stores, prompt engineering, FM customization, fine-tuning, and performance evaluation.
Guidelines for Responsible AI
14 scored + 0 pretest
Bias, fairness, inclusivity, robustness, safety, veracity, model transparency, explainability, human-centered design, and responsible model selection.
Security, Compliance, and Governance for AI Solutions
14 scored + 0 pretest
IAM, encryption, data lineage, secure data engineering, AI privacy risks, prompt injection, grounding, audit trails, governance, and compliance services.
Before you schedule
Confirm AIF-C01 is the selected exam, review Pearson VUE testing options, ID requirements, language availability, reschedule policy, and whether your AWS Certification account details match your ID.
Official Outline Coverage Map
Coverage is mapped to official outline item counts so content depth can be checked without hard-coding a single exam.
| Topic | Official outline items | Your questions | Your flashcards | Confidence |
|---|---|---|---|---|
| AI Terminology, Data and Learning Methods | 7 | 41 | 8 | Priority |
| AI Use Cases and AWS Managed AI Services | 7 | 40 | 8 | Strong |
| AI/ML Lifecycle, Metrics and MLOps | 6 | 40 | 8 | Priority |
| GenAI Foundational Concepts | 8 | 50 | 8 | Priority |
| GenAI Capabilities, Limitations and Business Value | 8 | 46 | 8 | Strong |
| AWS GenAI Services and Infrastructure | 8 | 48 | 8 | Priority |
| FM Design, RAG and Agents | 7 | 48 | 8 | Priority |
| Prompt Engineering Techniques | 7 | 42 | 8 | Strong |
| FM Training and Fine-Tuning | 7 | 38 | 8 | Good |
| FM Evaluation and Performance | 7 | 40 | 8 | Strong |
| Responsible AI Development | 7 | 50 | 8 | Priority |
| Transparency and Explainability | 7 | 34 | 8 | Strong |
| Securing AI Systems | 7 | 50 | 8 | Priority |
| AI Governance and Compliance | 7 | 34 | 8 | Strong |
How to use this guide
How to study for AWS Certified AI Practitioner
Treat each question as a business and governance decision: identify the AI pattern, choose the right AWS capability, then add responsible AI, cost, security, and evaluation controls.
1. Identify the AI pattern
Decide whether the scenario is prediction, generation, retrieval, recognition, automation, governance, or evaluation.
2. Choose the service or design path
Match the need to managed AI services, Bedrock, SageMaker AI, RAG, prompt engineering, fine-tuning, or monitoring.
3. Add responsible AI controls
Check fairness, transparency, explainability, grounding, human review, and business-value metrics.
4. Secure and govern the workflow
Apply IAM, encryption, data lineage, logging, audit trails, compliance services, and review cadence.
AI Terminology, Data and Learning Methods
AIF-C01 candidates need clean distinctions among AI, ML, deep learning, GenAI, agentic AI, inference modes, data types, and learning methods.
Key rules
Rule 1
AI is the broad field of systems that perform tasks associated with human intelligence; ML learns patterns from data.
Exam cue: Separate broad AI vocabulary from model training and inference behavior.
Rule 2
Deep learning uses layered neural networks, while GenAI creates new content from learned patterns and prompts.
Exam cue: Match labeled, unlabeled, structured, unstructured, image, text, and time-series data to the right model pattern.
Rule 3
Learning methods include supervised, unsupervised, and reinforcement learning, each matching a different data and feedback pattern.
Exam cue: Use the problem goal to distinguish classification, regression, clustering, and reinforcement learning.
Common traps
Calling every AI system generative AI.
Prevention: Avoid choosing GenAI because it sounds modern, trusting fluent output without validation, or ignoring privacy, cost, and governance requirements.
Treating unlabeled data as if it already contains the target answer.
Prevention: Avoid choosing GenAI because it sounds modern, trusting fluent output without validation, or ignoring privacy, cost, and governance requirements.
Confusing model training with inference.
Prevention: Avoid choosing GenAI because it sounds modern, trusting fluent output without validation, or ignoring privacy, cost, and governance requirements.
Memory anchors
Artificial Intelligence
AI is the broad discipline of systems that perform tasks associated with human intelligence.
Machine Learning
Machine learning uses data to train models that make predictions, classifications, or decisions.
Deep Learning
Deep learning uses multilayer neural networks to learn complex patterns from data.
Generative AI
Generative AI creates new text, image, audio, code, or other content from learned patterns.
Agentic AI
Agentic AI uses models, tools, memory, and workflow steps to pursue goals across actions.
Supervised Learning
Supervised learning trains from labeled examples that include the desired output.
Unsupervised Learning
Unsupervised learning finds patterns in data that does not include labels.
Reinforcement Learning
Reinforcement learning trains behavior through actions, rewards, and feedback.
Next best moves
Quick check-up
Use a short quiz to confirm the rule pattern is actually sticking.
Check-up Questions
Which statement best describes the relationship between artificial intelligence (AI) and machine learning (ML)?
A lender has historical applications labeled as either repaid or defaulted. The lender wants a model to classify new applications. Which learning method is appropriate?
Answer all questions to submit.
Next step personalized recommendations
Open another topic next
Official resources
Verify the details with the official sources
Use these links for eligibility, scheduling, handbook rules, and issuer updates. Our guide helps you study; official sources tell you what the testing partner currently requires.
AWS Certified AI Practitioner Exam Guide
Official AWS exam guide with AIF-C01 domains, task statements, scoring, and service references.
AWS Certified AI Practitioner
AWS certification page with exam overview, duration, format, cost, intended candidates, and preparation links.
AWS Certified AI Practitioner Exam Guide PDF
PDF version of the official AIF-C01 exam guide.
FAQ
Common AIF-C01 questions
Is this the official AWS exam?
No. These are original practice questions aligned to AWS's public AIF-C01 exam guide. They are not copied from secure exam material.
How many questions are on AIF-C01?
AWS lists 65 total questions, with 50 scored questions and 15 unscored questions, across multiple choice, multiple response, ordering, and matching item types.
What domains are tested?
The official guide lists Fundamentals of AI and ML 20%, Fundamentals of GenAI 24%, Applications of Foundation Models 28%, Guidelines for Responsible AI 14%, and Security, Compliance, and Governance for AI Solutions 14%.
What should I study first?
Start with AI/ML and GenAI vocabulary, then learn when to use Bedrock, SageMaker AI, RAG, prompt engineering, responsible AI controls, and governance services.
How should I use the 601 questions?
Use topic drills for weak concepts, section drills for the five official domains, and 100-question weighted mocks when you are ready to test timing and endurance.