How I Prepared for the AWS AI Practitioner Exam: A Step-by-Step Guide

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I recently completed the AWS AI Practitioner certification. In this post, I share the steps, resources and techniques that helped me deepen my understanding of key AI and AWS concepts – not just to pass the exam, but to sharpen how I approach real-world AI decisions.

Even with prior AWS experience using AWS services and leading AI initiatives, structured preparation proved valuable. It helped me consolidate knowledge and refine how I evaluate AI solutions – balancing trade-offs, business goals, cost, performance, and security at scale. This certification isn’t just about terminology or AWS services; it’s about learning to reason effectively about AI architectures.

1. Start with a Structured Course
Stéphane Maarek’s Udemy course provided a strong foundation, blending theory with hands‑on examples. If you take notes, I recommend using modular note cards so you can later connect ideas and organize the material in a way that fits your learning style.

2. Map the Big Picture
I created my own mental map, starting with the big picture before diving into specifics e.g. Bedrock vs. SageMaker vs. SageMaker JumpStart, model training vs. fine-tuning, ML vs. deep learning. I also paused frequently to refresh related concepts or explore adjacent services. For example, revisiting EC2 naturally led me to read more about Docker and Fargate, even though those weren’t in scope for the exam.

3. Take Your Own Notes
Writing notes by hand helped me internalize concepts more deeply (research shows handwriting engages more brain regions than typing). I revisited and enriched them along the way, adding details I initially missed or clarifying relationships between services as my understanding evolved.

4. Clarify the Full ML Workflow
I outlined the entire ML workflow – from data collection and preparation to model training, evaluation, selection, and deployment – and mapped the AWS features and services used at each stage e.g. SageMaker Data Wrangler for data preparation, SageMaker Clarify for model evaluation. This end-to-end view was critical for understanding how services fit together.

5. Check the AWS Exam Guide
I reviewed every concept and service listed in the official exam guide, focusing on purpose, differentiators, similar services, and how they integrate. This is key, as the exam expects you to architect real solutions using the full AWS ecosystem e.g. Lambda–Glue, Bedrock–Comprehend, Textract–Kendra.

6. Leverage AI as a Learning Assistant
I used ChatGPT to validate use cases, compare services (e.g. CloudWatch vs. CloudTrail vs. EventBridge vs. CloudWatch Alarms), and build associations such as thinking of NAT Gateways as “one-way hallways.” This helped reinforce understanding rather than rote memorization.

7. Get Hands-On with PartyRock (Optional)
For experimentation, I used PartyRock – a friendly interface built on top of Amazon Bedrock – to quickly prototype GenAI apps and explore how prompts and model outputs work. However, PartyRock doesn’t provide service integration or orchestration, which means real-world AWS AI solutions require additional tools e.g. Lambdas, APIs.

8. Practice in Exam-Like Conditions
AWS Skill Builder and Udemy practice exams were great for simulating the exam environment. Also, they helped me uncover knowledge gaps, think in terms of trade-offs, and get comfortable with how questions are framed.

9. Set Aside Focused Time to Study
After completing the Udemy course at a slower, more deliberate pace, I spent two weeks in an intensive, exploratory mode combining notes, practice tests, and ChatGPT “sparring”. This balance helped me stay exam-focused while still deepening my conceptual understanding.

10. Plan Your Exam Early
Test your Pearson Vue app and schedule the exam well in advance. In my case, all available slots for the following weeks were fully booked, leaving only a last-minute evening option – just one hour before the exam. Since I felt prepared, I chose to take it rather than wait several weeks, even though it meant shifting gears quickly.

***If you plan to take the exam by February 15, check the available discount code here.

Conclusion

Structured preparation isn’t just about passing an exam. It deepens your understanding of AI concepts, enhances your decision-making, and equips you to guide teams or clients with confidence. For me, this certification is far more than a badge – it sharpens my role as a tech leader and deepens my impact as a leadership coach, helping clients surface blind spots, challenge assumptions, and navigate complexity with greater clarity and intention.

Good luck!

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