Last month, I took the Azure AI Fundamentals certification after a couple of weeks of preparing for the exam. In this article, I go over my experience with the exam and provide a few tips for those planning on sitting this one, too.
Intro
Since the launch of ChatGPT, it’s hard to think of a topic that has gained more attention than generative AI. While there’s certainly been a lot of hype around AI potentially replacing jobs, there are also many practical use cases where AI can genuinely help us be more productive at work and in our personal lives.
I became an early Tabnine user in 2018 and have been interested in AI ever since. In 2020, my interest increased when I attended an AI and Machine Learning course at my University. With Microsoft being one of the key players in the Cloud space and making significant investments in AI, I decided to take this fundamentals exam to learn more about Azure AI services and revisit some of the foundational knowledge topics around AI and ML I learned in college.
Preparing for the Exam
I was able to apply for a 50% discount for the Azure AI fundamentals exam for attending one of Microsoft’s Virtual Training Days in February 2025, and I scheduled my exam for April. I studied for approximately 2 hours a day over two weeks, as I had a strong foundational knowledge of AI, and spent my time looking into Azure’s AI offerings instead. In terms of learning materials, I went through the recommended learning path on Microsoft Learn.
I took the sample exam in Microsoft Learn a couple of times, scoring above 80%, and felt comfortable taking the exam afterward.
The Exam
The exam consists of 40-60 questions to be completed in 45 minutes. It’s proctored, closed-book, and may include some interactive components.
I took the exam at a Pearson Vue in-person testing center. I’ve taken a few exams there, and the staff is helpful and friendly. The exam was relatively easy, granted it is a foundation-level exam. It accurately covered the exam guide topics, focusing on foundational AI knowledge and tying that to Azure AI services.
I scored 885 overall, with the following breakdown by section:
- Describe Artificial Intelligence Workloads and Considerations (15-20%) - 100%
- Describe Fundamental Principles of Machine Learning on Azure (20-25%) - ~85%
- Describe Features of Computer Vision Workloads on Azure (15-20%) - 100%
- Describe Features of Natural Language Processing (NLP) Workloads on Azure (15-20%) - ~80%
- Describe features of generative AI workloads on Azure (15-20%) - 80%
If you don’t come from a computer science background, I’d recommend going through the exam guide to learn more about how these AI systems work. If you decide to take the exam, I also highly recommend doing the hands-on labs in the Learning Path to help solidify the knowledge gained from the written documentation.
What’s Next
Moving forward, I plan to deepen my understanding of AI by reading some foundational books on the subject. I’m particularly interested in exploring “The Hundred-Page Machine Learning Book” by Andriy Burkov for a concise technical overview, and “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron for more practical implementation knowledge, to better understand the underlying principles that power Azure’s AI services.