Google Cloud Certified -Professional Machine Learning Engineer exam (2024)

I have successfully passed another Google certification exam called "Google Cloud Certified Professional Machine Learning Engineer". The certificate is available here.

Exam takes about 2 hours and consisted in my case of around 50 questions. I would say that the exam had quite a number of challenging questions and definitely needs some time to prepare.

In my case I have used Google Skills Boost and Coursera courses, specifically:

Google Skills Boost:

1. Machine Learning Engineer Learning Path available here.

Coursera:

1. Deep Learning Specialization available here.
2. ML Engineering for Production (MLOps) Specialization available here.

To completely finish the learning path above took me around 6 months of regular everyday studies of 1-2 hours per day and probably 2-3 hours on weekends.

Some notes and thoughts about the test and preparation for the exam:

1. GSB (Google Skills Boost) Path is very good for overall overview of the ML field, but will only be partially helpful preparing for the exam. I would not recommend taking all the courses and labs in the path to prepare for the exam, since it will take too much time.
2. Coursera specializations are excellent to understand the ML field even deeper and provides details on the DNNs (Deep Neural Networks), MLOps (Machine Learning Operations) and details about different architectures, such as CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), etc. However this information is only very partially helpful during the exam.
3. If the target is to pass the exam and not to deepen the AI/ML knowledge, I would recommend using slightly different approach, specifically I would:

1. Take example test (sample questions) available here
2. Understand which terms/topics I know well and which not
3. Understand all the right and wrong answers that I gave during the test
4. Collect information about all the terms that were not clear (that I did not understand or did not know)
5. Read definitions and brief articles about the terms and topics from the exam outline available here: and from the item 4 above
6. Read public documentation about the topics from 5, for example available here and pay special attention to best practices described for example available here
7. Definitely take detailed notes of what I have planned to read (I would recommend in electronic format and I will explain why below)
8. Definitely do tutorials, some are available here: and save the code for future use (unless available already at GitHub)
9. Use GenAI capabilities on your notes to create personalized quizzes, for example by feeding sections of your notes, dedicated to a specific topic to Bard/ChatGPT and asking the LLM to create multiple choice questions and answers based on the given text. These quizzes could be used to refresh information later by using specific quiz applications, such as Quizlet, Anki or similar.

Good luck with the preparation and the exam and I hope you would find my thoughts and suggestions useful!