How I Conquered the AWS AI Practitioner Exam in 24 Hours
Last week, I passed the AWS Certified AI Practitioner exam after just 24 hours of focused preparation. In this post, I’ll share my strategy and show you how you can replicate my results.
⚠️ Disclaimer: This approach isn’t recommended for everyone. I had prior experience with cloud computing and machine learning basics. Your mileage may vary.
The Pre-Game Setup
Before diving into the 24-hour challenge, I had these advantages:
- Basic understanding of cloud computing concepts
- Experience with core AWS services, including IAM, Amazon S3, and CloudTrail
- Understanding of generative AI fundamentals, experience with Amazon Bedrock, and knowledge of basic prompt engineering
- Basic awareness of AI/ML terminology from online articles and discussions, but no hands-on experience
If you’re starting from absolute zero, you might want to give yourself more time.
My purpose of taking this exam
My main goal for taking the AWS AI Practitioner exam was to systematically upskill myself in AI and cloud technologies. Here’s why this certification made perfect sense:
- Structured Learning Path: The certification provided a well-organized curriculum that helped identify and fill knowledge gaps in AI/ML concepts
- Industry Best Practices: Learning AWS’s approach to AI implementation exposed me to enterprise-level best practices and architectural patterns
- Skill Validation: The certification serves as a benchmark to validate my understanding of AI services and their practical applications
- Career Development: Adding AWS AI certification to my portfolio demonstrates commitment to staying current with emerging technologies
Through the certification process, I aimed to identify my knowledge gaps, including developing a deep understanding of AI, learning cost optimization strategies for AI/ML workloads, mastering integration patterns between AWS AI services, and understanding security considerations specific to AI/ML workflows. To address these gaps, I created a focused learning plan that went beyond just passing the exam.
The 24-Hour Game Plan
Hour 0-1: Strategic Planning
My first hour of the exam is to review the official AWS exam guide, identify key exam domains, and understand the exam structure.
- Review official AWS Certified AI Practitioner exam guide1
- Gather study materials
- Collect searchable practice exam question banks
Hours 1-6: Practice Tests for Gap Analysis
Instead of reading study materials, I jumped straight into active learning through active recall techniques2. I focused on taking practice exams and official exam simulations.
Don’t worry about getting many questions wrong at first. During these intense practice sessions, I identified knowledge gaps, flagged uncertain questions, and tracked incorrect answers to create a focused study plan. This approach helped me pinpoint exactly which topics needed more attention. The immediate feedback from practice tests was crucial for rapid learning and adaptation.
After completing these practice tests, I reviewed my wrong answers and focused on the topics where I consistently made mistakes. This targeted approach helped me make the most of my limited study time. I also used the AWS documentation to dive deeper into specific services that frequently appeared in the practice questions.
Hours 6-12: AI/ML Concepts and Best Practices
During this phase, I dove deep into the core AI/ML concepts essential for the exam. Instead of getting lost in theoretical details, I focused on practical understanding and real-world applications. With insights from my practice tests, I knew exactly which concepts needed the most attention.
The key fundamental concepts covered were:
- Machine learning workflow
- Types of learning (supervised, unsupervised, reinforcement)
- Model evaluation metrics
- AWS AI/ML security best practices
The AWS Certified AI Practitioner exam focuses primarily on choosing the right practices for each use case. My study plan concentrated on understanding these key differences:
- Supervised vs. unsupervised vs. reinforcement learning
- Prompt engineering vs. fine-tuning
- Understanding Amazon Bedrock’s LLM offerings vs. Sagemaker’s customizable models
- Metrics for evaluating AI/ML model performance, such as accuracy, precision, recall, and F1 score for classification tasks, or Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) for regression tasks
Another crucial topic is Sagemaker, AWS’s flagship machine learning service. It provides a comprehensive toolkit for the entire machine learning workflow, from data preparation to model deployment. While the practitioner exam doesn’t require deep technical knowledge, understanding its key components and use cases is essential.
Here are essential AWS AI services for the exam:
- Amazon SageMaker
- Amazon Rekognition
- Amazon Comprehend
- Amazon Lex
- Amazon Polly
- Amazon Textract
- Amazon Transcribe
- Amazon Translate
- Amazon Personalize
- Amazon Forecast
- Amazon Bedrock
Understanding these services’ core capabilities and ideal use cases was crucial for the exam. I focused on knowing when to use each service rather than memorizing all their features. I also created exam cheat sheet to quickly reference key concepts during my study sessions. This summary included essential service capabilities, common use cases, and key differences between similar services. Having this condensed reference helped me stay focused and efficient during my intensive study period.
Hours 12-24: Practice Tests again
After my break, I returned to intensive practice testing, this time focusing on simulating real exam conditions and timing. I repeatedly took same practice exams under strict time limits—just like the actual test. As I worked through more tests, I became better at spotting AWS’s distinctive question patterns and identifying likely correct answers.
I then tackled AWS’s official practice question sets, which introduced fresh scenarios and helped reinforce my learning. These questions were particularly valuable because they matched the real exam’s style and complexity, helping me identify and address any remaining knowledge gaps.
In the final hours, I concentrated on reviewing my incorrect answers, maintaining a detailed log of challenging topics to study between practice sessions. This systematic approach strengthened my understanding and boosted my confidence for the actual exam.
Exam Day
I took exam at my home using AWS’s online proctoring service (Pearson VUE). The experience was seamless, though I made sure to clear my desk and prepare a quiet space beforehand. Here are some key tips that helped me succeed on exam day:
- Check in early to avoid stress, as it takes 15-30 minutes to photograph your environment and surroundings
- Don’t spend too long on questions during your first pass. Instead, flag uncertain answers for review later.
- Double-check your answers before submitting your result, focusing on flagged questions.
I chose to take the exam in my preferred language, which helped me review questions more quickly. While the exam translations aren’t perfect, don’t worry—the exam interface has an option to view the original English version of each question.
The exam is 90 minutes long, but I completed it in just one hour. Within the next hour, my results arrived—I had passed and met all requirements. My focused preparation strategy and targeted practice had paid off.
Looking back, one exam question asked about connecting storage with SageMaker. I selected S3 since it’s the most common storage backend for AWS services. AWS Snowcone was listed as another option, but I wasn’t familiar with it (AWS has so many services!). While my intuition suggested trying Snowcone, I later learned this was a trick question. The takeaway? Don’t stress if you’re unsure about an answer—stick with the common solution and remember you have room for a few mistakes while still passing the exam.
Key Study Tactics
- Read AWS official exam guide
- Practice tests rather than reading
- Overview of unfamiliar features in the AWS Console and demo videos on YouTube for relevant AWS AI services to understand what problems each product solves
Final Thoughts
While I succeeded in passing the exam within 24 hours, this rapid preparation method is best suited for entry-level certifications. I recommend giving yourself more time to prepare if possible. The true value lies not just in passing the exam, but in developing a thorough understanding of how AWS AI services work in real-world scenarios.
🎯 Pro Tip: Focus on understanding the services’ use cases and limitations rather than memorizing every feature.
Next Steps
If you’re planning to take the AWS AI Practitioner exam, I’ve created a detailed cheat sheet as part of my AWS Certified AI Practitioner: Fast Track Exam Prep. It follows the same learning path I used and contains only the essential information needed to pass the exam—concise yet comprehensive. Check it out to accelerate your own certification journey.
References
