GOOGLE CLOUD

Google Cloud Professional Machine Learning Engineer

PROFESSIONAL-MACHINE-LEARNING-ENGINEER

The Google Cloud Professional Machine Learning Engineer certification validates your ability to design, build, and productionize machine learning models to solve business problems. It is for ML engineers and data scientists who architect scalable, reliable, and responsible ML solutions on Google Cloud. Earning it demonstrates you can turn ML experiments into secure, cost-effective, and maintainable production systems.

Practice Free Questions

6Domains

14Objectives

81Practice pages

381Free questions

PROFESSIONAL-MACHINE-LEARNING-ENGINEER free practice questions

Choose any objective and open any question page — no sign-in required.

Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

Building AI solutions using Google Cloud AI APIs or foundational models

16 questions · 4 pages

Questions 1–5 6–10 11–15 16–16

Exploring and preprocessing data for ML

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Model prototyping using notebooks (e.g., Gemini Enterprise Agent Platform Workbench and Colab Enterprise)

15 questions · 3 pages

Questions 1–5 6–10 11–15

Tracking and running ML experiments

29 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–29

Building models given the task considering cost, complexity, latency, and scalability

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

Training models

31 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–31

Choosing appropriate hardware for training

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

Serving models

40 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40

Scaling online model serving

37 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–37

Developing end-to-end ML pipelines

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Automating model retraining

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Identifying risks to AI solutions

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Monitoring, testing, and troubleshooting AI solutions

33 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–33

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (14 objectives · 81 pages).

Google Cloud Professional Machine Learning Engineer

PROFESSIONAL-MACHINE-LEARNING-ENGINEER free practice questions

Domain 1 · Architecting low-code AI solutions11% of practice bank · 2 objectives · 42 questions11%

Domain 2 · Collaborating within and across teams to manage data and models18% of practice bank · 3 objectives · 68 questions18%

Domain 3 · Scaling prototypes into ML models20% of practice bank · 3 objectives · 76 questions20%

Domain 4 · Serving and scaling models20% of practice bank · 2 objectives · 77 questions20%

Domain 5 · Automating and orchestrating ML pipelines15% of practice bank · 2 objectives · 58 questions15%

Domain 6 · Monitoring AI solutions16% of practice bank · 2 objectives · 60 questions16%