So, you’ve decided to tackle the Google Cloud Professional Machine Learning Engineer (PMLE) exam? Like many, I set myself the challenge, aiming to gain this valuable certification within a tight two-month window to support my company’s GCP partnership goals. It was a demanding goal, but I was determined to succeed.
In this guide, I’ll share the study strategies and resources that proved most effective in my preparation for the exam, and importantly, highlight areas where you shouldn’t waste your precious study time. Learning from others’ experiences is invaluable, and I found resources like this comprehensive repository of GCP certification posts incredibly helpful to gain diverse perspectives. I highly recommend checking it out before you dive into your studies.
And as is customary, here’s a glimpse of the certification I achieved:
GCP Professional Machine Learning Engineer Certification
Understanding the GCP Machine Learning Engineer Exam
The Professional Machine Learning Engineer certification exam is widely recognized as one of the most challenging Google Cloud certifications. This reputation stems from two key factors: the breadth of topics covered and the nuanced nature of the questions, where multiple answers might seem correct, but only one represents the best solution.
This exam isn’t just about theoretical knowledge; it’s about demonstrating your ability to apply Machine Learning techniques to solve real-world business challenges. It tests your understanding of how to leverage the most suitable Google Cloud Platform (GCP) solutions within the correct context.
The most crucial first step in your preparation is to thoroughly review the official GCP certification site. This page is your roadmap, detailing the exam scope, rules, testing locations, and other vital information. Understanding the exam objectives is paramount to focus your study efforts effectively.
Another excellent starting point is to take the sample questions provided by Google. This initial assessment, taken before any studying, will give you a baseline understanding of your current knowledge level and highlight areas that require your focused attention.
Recommended Experience for the Machine Learning Engineer Exam
While the official exam guide doesn’t explicitly list prerequisites, it does suggest:
3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.
My personal journey deviated from this recommendation. At the time of taking the exam, I had nearly a year of cloud experience (with AWS) but less than a month of hands-on experience with GCP. Here’s my perspective on the experience recommendation:
Years of experience are not always the best measure of knowledge. Meaningful experience, however, is. In my view, if you possess foundational cloud knowledge from any provider and grasp the core concepts and product functionalities, you are well-positioned to begin your preparation.
The role of a Machine Learning Engineer inherently involves problem-solving using ML models. This includes deploying models, managing data pipelines to feed those models, and establishing systems to consistently generate value from these solutions.
If you have practical experience in building machine learning models, your study focus can shift. If you can differentiate between problem types requiring classification, recommendation, or regression models, and understand when to apply Deep Neural Networks (DNNs) versus simpler models like Linear Regression, you can concentrate your study efforts on mastering GCP solutions for data serving and prediction delivery.
Key takeaways regarding prior experience:
- 3+ years of experience isn’t a strict requirement, but some cloud experience will significantly accelerate your learning.
- Machine learning experience is necessary, specifically the ability to design ML-driven solutions for business problems.
- Hands-on GCP experience, sufficient for exam preparation, can be acquired through Google-provided courses and labs.
Effective Study Strategies for the GCP ML Engineer Exam
The cornerstone of your exam preparation should be the suite of courses designed by Google and available on Coursera. However, not all courses are equally relevant to the exam content. Therefore, I’ve ranked them based on their importance and provided insights into each.
Before diving into the courses, let me share some techniques that greatly enhanced my learning process. If you’re solely interested in the course recommendations, feel free to skip ahead, but these methods significantly improved my ability to absorb and retain crucial information.
The central theme to keep in mind throughout your preparation is:
How to apply GCP solutions and ML models to address real-world business problems.
You need a comprehensive understanding of GCP’s ML and Data solutions – their functionalities, strengths, weaknesses, and ideal use cases.
Crucially, remember: While multiple solutions might technically work, the exam questions will always steer you towards the optimal solution within the GCP ecosystem.
To internalize these nuances, I employed two primary methods while engaging with the courses:
Flashcards for Solution Mastery
I utilized flashcards to memorize the specifics of each GCP solution: its function, key characteristics, and typical applications. I reviewed these flashcards repeatedly until I could confidently explain each solution without referring to the answers.
Flashcards are a powerful learning tool for several reasons. Creating them requires you to condense information into concise summaries, honing your ability to extract key details. Spaced repetition, reviewing flashcards at increasing intervals, strengthens long-term memory retention. Finally, explaining concepts aloud, as if teaching someone else, solidifies your understanding.
I personally used and recommend Anki, a free and highly effective flashcard application.
Mind Maps for Conceptual Connections
Another valuable technique for organizing complex information is creating mind maps. Mind maps visually represent the relationships between concepts, allowing you to easily connect GCP products and solutions with business problems and their respective advantages.
I used MindMeister, but numerous excellent free mind mapping tools are available.
Recommended Coursera Courses
Now, let’s explore the Google-provided Coursera courses and their relevance to the GCP Machine Learning Engineer exam.
Preparing for Google Cloud Machine Learning Engineer Professional Certificate
This Professional Certificate is the core of your preparation and demands your full attention.
It begins with foundational cloud concepts in Google Cloud Big Data and Machine Learning Fundamentals. If you’re already familiar with GCP data solutions, you might consider skipping this. However, for those new to GCP data services, this course provides an essential overview. It’s also one of the few courses that delve into data engineering solutions, making it valuable even for those with some GCP experience but lacking in data engineering knowledge.
The second and third courses introduce various GCP-managed ML solutions and pre-trained APIs. It’s critical to understand their functionalities and appropriate use cases.
The fifth, sixth, and seventh courses delve deeper into advanced ML solutions, feature engineering techniques, and model building products within GCP.
The final three courses—Production Machine Learning Systems, MLOps Fundamentals, and ML Pipelines on Google Cloud—focus on deployment strategies and building robust ML pipelines, incorporating best practices. In my opinion, these are the most critical courses for exam success.
All these courses include hands-on labs where you implement solutions in a live GCP environment. These labs are invaluable for practical learning and understanding how to configure and operate GCP services.
Some labs involve extensive Jupyter Notebooks with substantial code. In these cases, my advice is to focus on understanding the purpose of the code rather than getting bogged down in learning the specific syntax. If you need to implement similar code in the future, the GoogleCloudPlatform/training-data-analyst GitHub repository provides readily accessible examples and syntax references.
Course Study Summary:
- Employ flashcards, mind maps, or similar techniques to memorize details about GCP solutions.
- Prioritize MLOps and ML pipelines, but don’t neglect data engineering and core machine learning model concepts.
- Focus on understanding code functionality and benefits, not necessarily mastering syntax for the exam.
The Importance of Mock Tests and Exam Question Strategy
Mock tests are an indispensable part of your preparation. They serve to assess your knowledge retention and, equally importantly, to hone your exam question comprehension skills.
Mastering Question Answering Techniques
Your performance on the exam hinges on your ability to effectively analyze and answer questions. With 60 questions in 120 minutes, you have just 2 minutes per question. Efficiently reading questions and identifying key problem characteristics is crucial for selecting the correct solution. Let’s analyze an example question:
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real-time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
- A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
- B. Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.
- C. Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.
- D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.
The bolded text highlights the most critical information within the question. Pay close attention to keywords like “real-time,” “retrain,” “deploying,” and “end-to-end architecture.” The architecture requirement is particularly important as questions often specify constraints like “no-code,” “serverless,” or “full infrastructure control.” These constraints will guide you to the most suitable GCP service for the given scenario.
In this example, Kubeflow Pipelines is the only option that natively supports end-to-end workflows, including deployment and retraining. Therefore, option A is the correct answer.
A valuable exam strategy is to eliminate clearly incorrect answers after identifying the key information in the question. This narrows down your choices and simplifies the comparison process.
Recommended Mock Test Resources
I utilized a couple of mock test resources, although none are perfect. Be aware that some may contain incorrect answers. Here are my recommendations:
Exam Topics: This was the most beneficial mock test resource I used. While the website’s marked “correct” answers may not always be accurate, each question has a discussion forum where users present arguments for different answer choices. These discussions were a goldmine of insights and significantly deepened my understanding.
Google Sample questions: After completing your studies, revisit the initial sample questions you took at the beginning of your preparation. Your improved understanding should be evident.
While paid preparation exams exist, I haven’t personally evaluated them, as I relied solely on free resources. Some free sample questions from paid providers I encountered had questionable answers, so exercise caution.
If I were to prepare again, I would consider paying for full access to Exam Topics questions, focusing primarily on the user discussions when reviewing answers.
Thank you for reading, and best of luck on your journey to becoming a GCP Certified Professional Machine Learning Engineer!
If you found this guide helpful and want to support my work, you can buy me a coffee:
Gabriel Cassimiro is a Data Scientist sharing free content with the community
