Introduction
Recently, I earned the AWS Certified AI Practitioner (AIF-C01) certification, designed to validate foundational knowledge of AI/ML, generative AI technologies, and AWS services. This certification is ideal for those exploring AI/ML capabilities and how they apply to business solutions.
In this blog, I’ll share my preparation strategy, insights into the exam content, and resources that were instrumental in my success.
Exam Prerequisites
AWS recommends the following for candidates attempting this certification:
Experience: Up to six months of exposure to AI/ML technologies on AWS.
Knowledge: Familiarity with AWS core services (e.g., Amazon S3, AWS Lambda, Amazon SageMaker) and foundational cloud concepts such as the AWS shared responsibility model and AWS Regions.
Note: This certification focuses on understanding AI/ML and generative AI concepts, not building or optimizing AI models.
Exam Overview
| Level | Foundational |
| Length | 90 minutes |
| Cost | $100 USD |
| Format | 65 questions (multiple choice/response) |
| Delivery Method | Online proctored or testing center |
Exam Domains
| Domain | Weight |
| Fundamentals of AI and ML | 20% |
| Fundamentals of Generative AI | 24% |
| Applications of Foundation Models | 28% |
| Guidelines for Responsible AI | 14% |
| Security, Compliance, and Governance for AI | 14% |
How I Prepared
📚 Courses I Took
AWS Cloud Practitioner Essentials (AWS Training)
A great starting point for those new to AWS cloud concepts.
Introduction to Machine Learning (AWS Training)
This free course provides an overview of key AI/ML concepts.
Generative AI Fundamentals by AWS Skill Builder
Focuses on foundational generative AI concepts and services like Amazon Bedrock.
👉 Learn more about this course
🛠️ Hands-On Labs
Practical experience played a critical role in my preparation. Here’s what I worked on:
Building ML Models with Amazon SageMaker Canvas
- An easy-to-use no-code tool for creating ML models.
Deploying Foundation Models on Amazon Bedrock
- Worked on customizing pre-trained models for specific use cases.
You can replicate these labs using AWS Free Tier or tools like AWS Skill Builder.
📋 Study Groups and Communities
AWS AI and ML Community
- A supportive community for learning and asking questions.
AWS User Group Vadodara
- Engaged in insightful discussions about generative AI and related services.
Slack/Discord Study Groups
- Communities like Tech Study Slack helped me stay motivated.
📝 Practice Tests
Tutorials Dojo
Provides exam-specific practice questions with detailed explanations.
👉 Learn more
AWS Sample Questions
AWS offers free sample questions to familiarize yourself with the exam pattern.
🧠 My Study Plan
To stay on track, I created a structured study plan:
Week 1–2: Focused on AI/ML fundamentals.
Week 3–4: Explored generative AI concepts and use cases.
Week 5: Studied security, compliance, and responsible AI guidelines.
Week 6: Took mock tests and revised weak areas.
Exam Tips
Flagging Questions: Flag difficult questions to revisit later.
No Penalty for Guessing: Attempt every question, as incorrect answers don’t affect your score.
Focus on Key AWS Services: Prioritize services like Amazon SageMaker, Amazon Bedrock, and AWS Best Practice.
Time Management: Allocate time wisely to ensure you attempt all questions.
Additional Resources
AWS AI Ramp-Up Guide
A curated learning path for AWS AI services.
Machine Learning University (MLU)
Free courses by AWS on AI/ML topics.
Conclusion
The AWS Certified AI Practitioner exam is a fantastic starting point for anyone exploring AI/ML and generative AI on AWS. By following a structured study plan, leveraging hands-on experience, and using the right resources, you can successfully achieve this certification and build a strong foundation in AI.
Let me know if you found this blog helpful or have any questions about the exam in the comments below 👇.
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