Google Cloud Professional ML Certification: Everything You Need to Know from My Experience
In December 2024, I took the Google Cloud Professional Machine Learning exam and successfully earned the certification. After completing the certification, I wanted to share my preparation process and study notes.
In this post, I will cover the following topics in order. Enjoy the read!
- Introduction: How My Journey Began
- What is the Google Cloud Professional Machine Learning Certification?
- My Exam Experience
- Study Resources
- Final Thoughts & Small Tips
1. Introduction: How My Journey Began
Why Did I Decide to Pursue This Certification?
As a consultant, I believe that certifications are more than just badges you add to your CV. They serve as valuable tools to demonstrate expertise and stay up-to-date in the industry. Deloitte Partner Barış Yenidünya once shared a great analogy on this topic:
“Having a certification doesn’t necessarily mean you’re an expert in the field — just like having a driver’s license doesn’t mean you’re a great driver.”
That being said, obtaining this certification is still important because it validates your knowledge of Google Cloud’s machine learning services.
I have been working in data science for nearly six years, and it took me about 2–3 weeks to prepare for this certification. However, the preparation time can vary depending on your familiarity with Google Cloud and machine learning concepts.
2. What is the Google Cloud Professional Machine Learning Certification?
This certification tests competencies in designing, training, and managing machine learning models on Google Cloud. It covers the following key topics, and for a more detailed list, you can refer to the topic outline published by Google Cloud:
- Data preprocessing and feature engineering
- Model development and optimization
- ML pipeline automation
- Model deployment and monitoring
- Responsible AI and security
Who Is It For?
- ML engineers and data scientists
- Cloud engineers working on ML projects in Google Cloud
- Consultants looking to run ML models at scale
Exam Format
- Duration: 120 minutes
- Number of Questions: 50–60 (Although Google Cloud states this range, based on my experience, the exam consisted of 50 multiple-choice questions.)
- Question Type: Multiple-choice
- Exam Location: Online or at a test center (can be selected during purchase)
- Validity Period: 2 years
Google Cloud periodically updates its exam questions and study materials. The version I took had just been updated and included questions on Generative AI-focused services.
3. My Exam Experience
Exam Day Experience
Online Participation: The exam is purchased through Webassessor. A stable internet connection, a webcam, and a quiet environment are required during the exam. Additionally, no written materials, pens, or paper are allowed on your desk. The proctor verifies all these conditions before the exam begins.
Note-Taking Restriction: In my previous Google Cloud Digital Leader exam, there was a note-taking area on the exam screen. However, this exam did not provide such an option. Since using pen and paper was also prohibited, all calculations and thought processes had to be done mentally.
My Approach to Answering Questions
Focus on the Scenario, Not the Story: The questions are generally long and presented in a scenario-based format. My approach was to focus on the critical points rather than the narrative details.
For example, if a question mentions a large enterprise, concepts like scalability and big data should immediately come to mind. Instead of getting caught up in unnecessary details, extracting the key technical aspects proved to be highly effective.
Match Services with Key Features: When answering questions, matching Google Cloud services with their key attributes was incredibly helpful, especially for handling tricky questions.
Since many services in Google Cloud share similarities, I focused on their subtle differences and the specific scenario given in the question. For example:
- BigQuery vs. Cloud SQL → Is the focus on big data analytics or transactional data?
- Dataflow vs. Dataproc → Is it about streaming data processing or batch data processing?
- Vertex AI vs. AI Platform → Is model training or scalability the priority?
This approach was particularly useful when I was stuck between two possible answers.
4. Study Resources
My Notes and GenAI Support
My most valuable study resource was the notes I created while discussing topics with ChatGPT. I asked ChatGPT about each topic outlined by Google Cloud, explored the details, and then had it generate cheat sheet-style summaries after learning each concept.
Creating a summary sheet for every topic helped me distill key information into bite-sized insights, making exam preparation much more efficient.
I am openly sharing all my notes on GitHub. These notes emphasize the key features of Google Cloud services and serve as quick reminders. The notes are a mix of Turkish and English.
Official Google Cloud Resources
Google Cloud offers free courses specifically designed for this certification. While I didn’t go through the lab exercises myself, I recommend starting with these courses if you’re new to the topic. For this reason, I’m also sharing Google Cloud’s official courses as a primary resource.
Additionally, Google Cloud provides insights into key exam topics, question distribution, and other relevant details, which can be helpful for structuring your study plan.
Examtopics:
- Examtopics is a resource that contains scenario-based questions similar to those in the actual exam. However, keep in mind that some questions may have incorrect answers. To get the most out of it, I recommend discussing and verifying the answers rather than relying on them blindly. This approach helps reinforce the correct concepts while avoiding potential misinformation.
5. Final Thoughts & Small Tips
Below is a summary of the key points I covered in this post, along with some additional quick tips:
Exam Experience May Vary
Your experience might be different from others taking the exam at the same time. When I took the exam, three of us attempted it within the same period, and we all had different perceptions of difficulty, question length, and content.
The only common factor among us was our study methods and notes. While exam experiences are valuable, it’s important to remember that someone who found the exam extremely difficult — or even failed — had a personalexperience that may not apply to you.
Keep Up with Updates
As I mentioned earlier, exam questions and content evolve alongside changes in Google Cloud services.
Before taking the exam, make sure to review the latest course topics, question distribution, and exam content provided by Google Cloud. Studying the most up-to-date materials will help you focus on relevant areas and avoid outdated information.
Understand the Differences Between Services
Many Google Cloud services have similar functionalities, but small distinctions — such as data size, real-time vs. batch processing — make a big difference in selecting the right one. Knowing these differences well can make the exam much easier.
Creating a summary sheet that highlights the key differences between similar services can be extremely useful during your preparation.
Final Words
The Google Cloud Professional ML Engineer Certification is a valuable credential for those working with machine learning on Google Cloud. For me, its biggest benefit was gaining a deep understanding of Google Cloud’s ML services and learning how to apply them in a professional setting.
During my preparation, my notes and scenario-based practice questions were the most effective study tools. If you’re preparing for this exam, I highly recommend focusing on understanding scenarios and learning the key differences between services.
Lastly, I’d like to thank my amazing study partners, Sude Külek and Alperen Gazi Cesur — preparing together made the journey even more enjoyable!
Thanks also to Seçkin Görkem Okkar for all his support! ❤
I hope this post has been helpful and serves as a guide on your exam journey. Feel free to share any additions or comments, and if you enjoyed this post, a few claps would mean the world to me! 🎉
If you’d like to connect with me, follow my work, or get in touch, you can visit my website. Feel free to follow me on social media, especially on Instagram, and reach out anytime. Thank you!
