1. Exam Overview
The AWS Certified AI Practitioner – AIF-C01 exam validates foundational knowledge of AI, ML, and generative AI concepts.
It is designed for individuals with limited AI/ML experience who want to understand how to apply AI responsibly using AWS services.
📘 Official Exam Guide (PDF): AWS Certified AI Practitioner Exam Guide
🌐 Official Certification Page: AWS Certification – AI Practitioner
🧩 Practice Questions: Cloud Pass AIF-C01 Practice Page
Exam Details
- Questions: ~65
- Duration: ~90 minutes
- Experience: Up to 6 months of exposure to AWS AI/ML tools
- Focus Areas: AI/ML basics, generative AI, foundation models, responsible AI, and governance
2. Exam Domains
| Domain | Description | Weight |
|---|---|---|
| Domain 1: Fundamentals of AI and ML | Core AI/ML concepts, learning types, use cases | ~20% |
| Domain 2: Fundamentals of Generative AI | Tokens, embeddings, prompt design, capabilities and limitations | ~24% |
| Domain 3: Applications of Foundation Models | RAG, vector databases, deployment considerations | ~28% |
| Domain 4: Responsible AI Guidelines | Ethics, bias, explainability and accountability | ~14% |
| Domain 5: Security and Governance for AI Solutions | IAM, encryption, data protection, compliance | ~14% |
3. Study Strategy
(1) Understand the AI/ML and Generative AI Lifecycle
Learn the end-to-end process:
Data Preparation → Model Selection & Training → Generative AI Usage → Deployment & Monitoring → Responsible Governance
(2) Focus on Core AWS Services
- Foundational: Amazon S3, EC2, AWS Lambda, Amazon SageMaker
- Generative AI & Foundation Models: Amazon Bedrock, SageMaker JumpStart, Amazon Q, vector DB services (OpenSearch, Neptune)
- Responsible AI & Security: IAM, KMS, CloudTrail, audit logging
(3) Practice Problem Solving
Focus on why each approach is appropriate. Ask:
- Which AI method best fits this scenario?
- What risks should be considered with generative AI?
- How do we ensure ethical and secure AI design?
👉 Cloud Pass AIF-C01 Practice Page
(4) Review AWS Whitepapers and Best Practices
Use the official exam guide and AWS documentation to reinforce understanding of AI principles and real-world applications.
4. Key AWS Services Summary
| Area | Services | Key Points |
|---|---|---|
| AI/ML Basics | SageMaker, S3, Lambda, EC2 | Difference between AI, ML, DL; supervised vs unsupervised learning |
| Generative AI | Bedrock, SageMaker JumpStart, Vector DB, RAG | Prompt design, embedding usage, model limitations |
| Responsible AI | Bias, Explainability, Accountability | Ethical and sustainable AI development |
| Security & Governance | IAM, KMS, Logging & Compliance | Data security and policy enforcement |
5. Common Exam Scenarios
- Designing a safe prompt workflow for a generative AI chatbot
- Using foundation models for summarization or recommendation
- Improving AI transparency through explainable model reporting
- Applying AI governance in regulated industries
6. Study Roadmap
| Week | Goal | Focus |
|---|---|---|
| Week 1 | Understand exam domains and structure | Review official guide and objectives |
| Week 2 | Build AI/ML and Generative AI concepts | Tokens, embeddings, prompts |
| Week 3 | Explore Foundation Model applications | Bedrock, JumpStart, vector databases |
| Week 4 | Study Responsible AI and Governance | Ethics, bias, IAM/KMS integration |
| Week 5 | Take practice tests | Cloud Pass mock exams and review weak areas |
7. Final Tips
- Always ask “why” behind each AI design choice.
- Combine theory with practice for deeper understanding.
- Manage time efficiently — questions may be long and scenario-based.
- Review ethical and security trade-offs for each AWS service.
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