Building GCP ML Engineer Mock Exam App with GitHub Pages
If you’re preparing for the Google Cloud Certified - Professional Machine Learning Engineer exam and want a structured, interactive way to practice, I’ve built a free mock exam tool — and you can too! Here’s how I created a professional, responsive mock test web app hosted on GitHub Pages using Jekyll, YAML, and TailwindCSS.
💡 Why I Built This
As I was preparing for the GCP ML Engineer exam, I noticed a lack of high-quality, interactive mock exams — especially ones that are open source and customizable. So I decided to build one myself with a few goals in mind:
- ✅ Structured questions based on the official exam guide
- ✅ Filterable by tags and sections
- ✅ Timer-based exam simulation
- ✅ Immediate feedback and score
- ✅ Review incorrect answers and download as PDF
- ✅ Hosted freely using GitHub Pages
🔨 Tech Stack
- Jekyll (for static site generation)
- TailwindCSS (for styling)
- JavaScript (no frameworks)
- YAML (for storing questions by section)
- GitHub Pages (for free hosting)
🧠 Features
| Feature | Description |
|---|---|
| 🧪 Full & Quick Exam Modes | Simulate a 120-minute full exam or a 30-minute practice sprint |
| 🏷️ Tag Filtering | Filter questions by topics like Vertex AI, Feature Engineering, etc. |
| 📋 Section-Based Sampling | Questions weighted by domain (%), as in the official exam guide |
| ⏰ Timer & Auto-Submit | Countdown timer with automatic submission on timeout |
| 📊 Summary View | Full answer breakdown with highlights for correct/incorrect choices |
| ⬇️ PDF Download | Print-friendly summary to save results |
| 🧩 Review Incorrect Only | Focus your prep on the questions you missed |
🖼️ A Visual Overview
Welcome page
Selecting Exam Mode
🚀 Try the App
👉 Launch Mock Exam App
No login. No cookies. Just practice.
📁 How It Works
Folder Structure
ml-cert-mock-exam/
├── index.html # Landing page
├── mock-test.html # Main app
├── _data/sections/ # YAML files for question banks
├── README.md
├── CONTRIBUTING.md
└── thumbnail.png
YAML Example (section1.yml)
- question: "When leveraging BigQuery ML, which consideration focuses on defining the problem structure and desired outcome for the model?"
options:
- Building the appropriate BigQuery ML model based on the business problem
- Optimizing hardware accelerators
- Integrating with external data sources
- Monitoring model performance in real-time
answer: 1
tags:
- BigQuery ML
- Business Problem
💻 Want to Build One Yourself?
The entire project is open source.
You can fork it, extend it with your own questions, or use it for other certifications (like AWS, Azure, or Kubernetes).
📢 Disclaimer
This project is not affiliated with or endorsed by Google.
All questions are original, inspired by the official exam guide and intended for learning purposes only.
🙌 Final Thoughts
I hope this tool helps you gain confidence for your exam day. If you find it useful, feel free to ⭐️ star the repo or suggest improvements via a pull request.
Good luck on your ML journey — and may your models always converge!
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