AIGoogle CloudPMLE

Google Cloud Professional Machine Learning Engineer

Exam overview, official domain weights and free sample questions. Subscribers get 230 practice questions, 97 flashcards, 3 labs, a study guide and video lessons for this exam.

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Exam at a glance

Exam code: PMLE
Questions: 50–60
Time: 120 minutes
Question types: multiple choice, multiple select
Passing score: not published
Exam fee: 200 USD (plus tax where applicable)
Delivery: online proctored, onsite at a test center
Prerequisites: None. Recommended: 3+ years of industry experience, including 1+ year designing and managing solutions on Google Cloud
Renewal: Valid 2 years. Can be renewed through Google Skills courses or skill badges in the final year (+1 year)
Version notes: not published

From the official exam page (checked 2026-11-09). Always confirm details with the vendor: official exam page, official objectives.

Domains and weights

Architecting low-code AI solutions13%

1.1 Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform
1.2 Building AI solutions using Google Cloud AI APIs or foundational models

Collaborating within and across teams to manage data and models16%

2.1 Exploring and preprocessing data for ML
2.2 Model prototyping using notebooks
2.3 Tracking and running ML experiments

Scaling prototypes into ML models21%

3.1 Building models given the task considering cost, complexity, latency, and scalability
3.2 Training models
3.3 Choosing appropriate hardware for training

Serving and scaling models20%

4.1 Serving models
4.2 Scaling online model serving

Automating and orchestrating ML pipelines18%

5.1 Developing end-to-end ML pipelines
5.2 Automating model retraining

Monitoring AI solutions13%

6.1 Identifying risks to AI solutions
6.2 Monitoring, testing, and troubleshooting AI solutions

Free sample questions

Original questions in the style of the exam. Pick an answer to see the explanation.

Sample question 1 · Architecting low-code AI solutions

Analysts keep sales data in BigQuery and want to build a churn classifier using only SQL, without moving data. What should they use?

Sample question 2 · Collaborating within and across teams to manage data and models

A data engineering team must run the same preprocessing logic on streaming and batch data at scale with autoscaling workers. Which service fits?

Sample question 3 · Scaling prototypes into ML models

A team must train a PyTorch model with a custom architecture on Agent Platform. Which approach fits?

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