What Sparked My Journey

My journey into AWS certification began during a regular mentoring session with my mentor, a cloud architecture expert. During our discussions about the changing world of data science, his knowledge and excitement about cloud technologies got me interested in AWS certifications.

Looking at job postings, I noticed that experience with cloud platforms like AWS and Azure often appeared as a plus. Even though these weren’t required skills, I knew having cloud expertise would make me a stronger candidate for data science and ML engineering roles.

As someone planning to move to Canada, I had another reason to pursue these certifications. Since my education and work experience were all in Korea, I needed credentials that would be recognized worldwide. AWS certifications fit this need perfectly, offering globally respected proof of my cloud computing skills.

Starting with AWS Cloud Practitioner

When I began studying for my first certification, everything felt new. I had used cloud-based services like Gmail, iCloud, and Netflix before, but I had never learned about the technology that makes them work. To my surprise, I really enjoyed the learning process. I had been feeling a bit tired of the usual interview prep and coding tests, so this new challenge was exactly what I needed. It reminded me how much I enjoy learning new things.

I earned my Cloud Practitioner certification in one week, but I knew I wanted to learn more. While I had learned the basics through free resources and tutorials about services like EC2, AWS Lambda, and S3, I wanted to go deeper. As a data scientist, I was especially interested in using AWS services for MLOps and AI applications. When I looked at the AWS Certification Path for Data Scientists, I found the AI Practitioner certification — it seemed perfect for what I wanted to learn. Since I got a 50% discount coupon after my first certification, I signed up for the second exam right away.

Pursuing the Second Certificate: A Focus on Practical Applications

The AI Practitioner certification showed me how valuable AWS services can be for individual data scientists. What really caught my attention was AWS’s pay-as-you-go pricing, which means even individual developers can use powerful MLOps tools. Through Amazon SageMaker, I learned I could build, deploy, and manage ML models from start to finish without spending a lot of money upfront.

I also learned about Amazon Bedrock for AI applications. While using pre-trained models or building RAG (Retrieval-Augmented Generation) systems might be too expensive for personal projects, learning about these services gave me many ideas for future work. This practical knowledge turned out to be even more valuable than the certification itself.

Learning Strategy and Resources

Getting both certifications quickly worked well because I used different resources and languages strategically. While studying for the AI Practitioner certification, I mostly used English materials since I would take the exam in English. But I found that when I was tired, especially during late-night study sessions or after working out, switching to Korean resources helped me stay focused. The Korean materials on AWS Skill Builder were really helpful during these times.

The Udemy courses by Stephane Maarek made a big difference in my learning. Seeing the same concepts explained in different ways helped me understand them better through repetition and different perspectives.

Top Resources That Made the Difference

1. AWS Certified AI Practitioner (AIF-C01) Exam Guide: Unlike the Cloud Practitioner certification that covers most AWS services, the AI Practitioner certification focuses on specific services. The exam guide helped me understand exactly what to study. Don’t worry if you’re new to AI — the machine learning questions are at a basic level. For example, you might need to choose appropriate evaluation metrics for regression models or select the right metrics for specific situations.
2. Tech With Lucy’s YouTube Video: “I Just Passed the AWS AI Practitioner Certification Exam!”: After watching many videos, this one stood out as the most concise and well-organized guide to the certification process.
3. Stephane Maarek’s Ultimate AWS Certified AI Practitioner Course on Udemy: This course feels like having a great private tutor. What I loved most were the service demos. While you could try the services yourself, watching demos first helps avoid unexpected charges from clicking around AWS services before understanding the pricing model. These demos really helped me understand how to use AWS services in future projects.
4. Practice Exams: AWS Certified AI Practitioner — AIF-C01: I used practice exam sets with 85 questions each, which helped me better understand both the exam style and the underlying concepts.

Tips from My Experience

1. Don’t Rush the Process: When I was preparing for the Cloud Practitioner exam, I focused too much on getting certified quickly. With the AI Practitioner certification, I took a different approach. I spent time exploring the services, watching demos, and enjoying the learning process. This made the journey much more valuable.
2. Follow the AWS Certification Path: There’s a good reason AWS suggests starting with Cloud Practitioner before AI Practitioner — the knowledge builds up naturally. Many concepts from the first certification appeared in the AI Practitioner exam, making it easier to understand the more advanced topics.
3. Use Multiple Learning Resources: Combining different resources helped me see concepts from various angles. The AWS official materials felt like textbooks, while Udemy courses were more like having a personal tutor. Using both helped reinforce my understanding.

Real Value Beyond Certifications

These certifications gave me much more than just credentials. They helped me build a strong foundation in cloud services and AI technologies while reviewing concepts I already knew. Best of all, they gave me practical ideas for using AWS services in my own projects. I’ve already started working on a data science project using Python and AWS services, putting what I learned from the certification demos into practice.

Future Plans

My next step is to actually use AWS services in my personal projects. Getting the certificates was just the beginning — now I want to put all this knowledge to work. I’m also planning to pursue the AWS Certified Machine Learning Engineer — Associate certification next. This certification digs deeper into SageMaker, covering the complete MLOps process from start to finish.

Conclusion

If you’re interested in AWS certificates or learning about cloud services, AWS Cloud Practitioner is a great place to start. AWS offers excellent free learning resources, and you can find lots of helpful content on YouTube and other platforms. While I chose to use some paid resources to make sure I passed on my first try, there are many ways to prepare for these exams successfully.

Remember, the real value isn’t just in passing the exams — it’s in understanding how these services can help you build real projects. Take time to explore each service and enjoy the learning process. Whether you’re aiming for one certification or several, the knowledge you gain will be invaluable for your tech career.

Here’s a helpful tip: When registering for AWS Cloud Practitioner or AWS AI Practitioner exams, you can use the code ‘AWSRetake2025’ (valid from October 9, 2024, to February 15, 2025) to get a free retake if you don’t pass the first time. This makes it a perfect time to give it a try!