Cracking the GCP Certified Professional Machine Learning Engineer Exam in a Week

7 min readJan 11, 2024

Hey there! As someone who is a software engineer by profession and a machine learning (ML) engineer by passion, I’ve always been involved in projects that require a strong understanding of software engineering concepts like architecture, API design, testing, performance optimization, and scalability, while also building robust machine learning systems. It’s been a wild ride of over 7 years in ML, and in the last couple of years, I’ve been diving into cloud platforms like GCP and Azure, building machine learning systems there.

So, my company recently needed some of us to get GCP certified, kind of a deal we have going on for a partnership. I had a heads-up of about ten days, but I decided to go for the GCP Professional Machine Learning Engineer certification. Why? It just seemed right up my alley, based on the exam topics. In this blog, I’m going to spill the beans on how I cracked this exam in less than a week. Whether you’re just starting out or you’ve been around the block a few times, I hope this blog will give you a leg up on your own certification quest. Let’s jump right in!

Laying the Foundation

Alright, let’s dive in! When it comes to a machine learning certification, having a rock-solid understanding of ML is key. Personally, since I spend a hefty chunk of my time getting my hands dirty with practical ML work, I skipped over revising the basics. But, if you’re not knee-deep in ML day in and day out, you might want to brush up on some essential concepts, particularly if your hands-on experience is a bit on the lighter side. Here’s a more detailed breakdown of the areas you should be comfortable with:

Performance Metrics: Really get to grips with recall, precision, PR curves, and ROC-AUC. Understanding these metrics is fundamental for accurately evaluating the performance of your ML models. Dive into how each metric works and under what scenarios they are most effective.
Loss Functions: Reacquaint yourself with common ones like cross-entropy or mean squared error, as well as others like hinge loss or log loss. These are the driving force behind how your ML models learn during training. Each loss function has its specific use case, depending on the type of problem you’re solving (classification, regression, etc.).
Activation Functions: Take a deeper look into functions like ReLU, Sigmoid, Tanh, and others like Leaky ReLU or Softmax. These functions determine the output of a node in a neural network and hence are crucial in defining the behavior of your model.
Bias vs. Variance: Thoroughly understand this trade-off, as it’s central to building effective and reliable ML models. Know how to diagnose and address underfitting and overfitting in your models.
Feature Engineering and Selection: Delve into the techniques for selecting the right features and transforming raw data into a format that is better suited for ML models. This includes understanding dimensionality reduction techniques like PCA (Principal Component Analysis).
Model Evaluation: Besides performance metrics, familiarize yourself with different model evaluation techniques like K-fold cross-validation, train/test split, and bootstrapping.
Ensemble Methods: Understand the nuances of ensemble methods like Bagging, Boosting, and Stacking. Know how these techniques can improve model performance by combining multiple models.
Machine Learning Algorithms: Review a range of algorithms from linear regression, logistic regression, decision trees, and SVMs (Support Vector Machines) to more advanced ones like neural networks, and clustering algorithms like K-means.
Deep Learning Concepts: If your certification covers deep learning, make sure to review topics like CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and techniques like dropout and batch normalization.
Machine Learning with Big Data: Grasp how ML scales to big data, and familiarize yourself with concepts like online learning, and distributed ML frameworks like TensorFlow or PyTorch.
Additional Topics: Depending on the certification, you might also need to brush up on topics like Natural Language Processing (NLP), Reinforcement Learning, Explainability (SHAP, LIME, Integrated Gradients, etc.), or specific ML platforms and tools (What If, LIT).

This review could take a day or more, depending on your current knowledge level. But it’s a critical step to make sure you’re fully prepared for the foundational aspects of the exam. Remember, being thorough with these concepts not only helps in the certification but also strengthens your overall understanding of machine learning.

Gaining GCP Experience

Over the past two years, my machine learning (ML) projects have been deeply intertwined with the Google Cloud Platform (GCP), granting me extensive hands-on experience with its array of services. However, if you’re not as well-versed in GCP, it’s vital to dedicate some time to familiarize yourself with its key services, which are integral for the exam:

Compute Engine, Pub/Sub, Cloud Function, Cloud Run, Cloud Build: These services are the cornerstone of computing and processing within GCP. They offer a range of capabilities from virtual machines and event-driven functions to fully managed build and deployment services.
BigQuery: This is Google’s serverless data warehouse, which is indispensable for managing large datasets. It allows for fast SQL queries and is highly scalable, making it a go-to for big data analytics.
Google Cloud Storage (GCS): A fundamental service for data storage and retrieval. Its robustness and scalability make it suitable for storing vast amounts of data in various formats.
Google Kubernetes Engine (GKE), Artifact Registry: These are crucial for the deployment and management of containerized applications. GKE provides a managed environment for deploying, managing, and scaling your containerized applications using Google’s infrastructure.
Vertex AI: Pay special attention to its components like Pipelines, AutoML, Custom Training, Endpoints, and Batch Prediction. Vertex AI integrates various ML tools and makes it easier to deploy and maintain AI models.

Once you’re comfortable with these core services, it’s beneficial to expand your knowledge to include other GCP products like Video AI, Vision AI, Text-to-Speech API, Natural Language AI, Recommendations AI, Generative AI, Dialogflow, and Search & Conversation. A thorough understanding of how these services work together and their practical applications is crucial for the certification exam. You can get a very high level overview and use-cases of different GCP products from here: https://cloud.google.com/products#featured-products/

It’s important to not only know what each service does but also understand when and how to use them effectively in real-world scenarios. This comprehensive grasp of GCP services will not only help you in the certification exam but also enhance your capabilities in handling ML projects on the cloud platform.

A High-Level Overview

Before diving into the study, it’s beneficial to get a high-level overview of the different products offered by GCP. The GCP product page (https://cloud.google.com/products#featured-products/) is an excellent resource for this. It helps in categorizing and understanding the wide array of services and products available.

The 7-Day Study Plan

Given my background and familiarity with Google Cloud Platform (GCP), I initially outlined a 10-day study strategy. However, due to unexpected time constraints, I had to compress my study period into just five days, followed by two days dedicated to intensive practice and review. Here’s a detailed breakdown of my approach:

Day 1–2: I started by quickly going through the book “Journey to Become a Google Cloud Machine Learning Engineer: Build the Mind and Hand of a Google Certified ML Professional”. On the first day, I skimmed through the content, marking sections that were unfamiliar. The second day was spent in a deep dive into these marked sections. This book provides a basic overview, but it’s not exhaustive enough for complete exam preparation.
Day 3: This day was devoted to thoroughly examining the Certification Exam Guide. I reviewed the GCP documentation for each topic listed in the guide, focusing particularly on the overviews, practical use cases, and how different services integrate within the GCP ecosystem.
Day 4–5: These two days were all about hands-on practice, primarily through labs from the Machine Learning Engineer Learning Path. To optimize my time, I watched the instructional videos at double speed, choosing to skip the longer ones. I made sure that I completed all the quizzes to test my understanding. I only went through 10–15% of the materials, as most of the courses were quite lengthy and not alien to me. If you feel you need more hands-on experience, you can spend more days on this segment.
Day 6: I dedicated this day to practice with sample questions. Additionally, I sought out other resources such as YouTube tutorials and websites like gcp-examquestions.com and examtopics.com to find more questions to practice. I also tried practicing from skillcertpro.com but found that many of their questions were repetitive and some answers were incorrect, so I wouldn’t recommend it.
Day 7: The final day was reserved for a comprehensive review of all the topics. This was a critical phase where I consolidated my learning and reinforced key concepts to ensure I was fully prepared. I also went through many medium articles to go through others study plans and see if I missed any topic.

Cracking the GCP Certified Professional Machine Learning Engineer Exam in a Week

Laying the Foundation

Gaining GCP Experience

A High-Level Overview

The 7-Day Study Plan

Helpful Resources

Written by Zabir Al Nazi Nabil