GCP ML Engineer Exam: Structure and Question Types
A detailed breakdown of the Google Cloud Professional Machine Learning Engineer exam structure, question formats, and domain weightings.
Understanding the Exam Structure
The Google Cloud Professional Machine Learning Engineer exam is designed to test your practical ability to build and maintain ML solutions on GCP. Here’s what you can expect:
- Exam Length: 2 hours
- Number of Questions: 50-60 questions
- Question Format: Multiple choice and multiple select
Question Types
The questions are scenario-based, requiring you to apply your knowledge to solve real-world problems. You will not be asked simple "what is" questions. Instead, you'll see questions like:
- "Given a scenario, what is the most cost-effective way to train a model?"
- "Your model's performance is degrading in production. What is the most likely cause?"
Domain Weightings
The exam is divided into six domains, each with a different weight:
| Domain | Weight |
|---|---|
| 1. Architecting Low-Code AI Solutions | ~13% |
| 2. Collaborating on Data and Model Management | ~16% |
| 3. Scaling Prototypes into ML Models | ~18% |
| 4. Serving and Scaling Models | ~19% |
| 5. Automating and Orchestrating ML Pipelines | ~21% |
| 6. Monitoring ML Solutions | ~14% |
As you can see, the most critical areas are ML pipelines, model serving, and model development.
Knowledge Check
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