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Free Google ML Engineer Practice Test (2026) - Google Professional Machine Learning Engineer Questions

Google ML Engineer Practice Test

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Questions updated at Jul 10, 2026, 12:01 AM CDT

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Question 1 of 10

Objective GPMLE-3.1 Model Development and Evaluation

A demand-forecasting model needs to predict revenue for each store and week. Which modeling task is most suitable for this scenario?

Concept tested: Model Development and Evaluation (GPMLE-3.1)

Source:Professional ML Engineer exam guide

Question 2 of 10

Objective GPMLE-5.2 Serving, Scaling, and Monitoring

An energy team needs to score millions of meter records overnight and write the results back to storage. Which prediction mode should the engineer choose?

Concept tested: Serving, Scaling, and Monitoring (GPMLE-5.2)

Question 3 of 10

Objective GPMLE-4.1 MLOps, Pipelines, and Automation

A supply-chain model build requires a managed workflow that automates the entire process, from data preparation and training to model evaluation and deployment. Which Vertex AI capability should the engineer utilize to orchestrate these steps and ensure repeatability?

Concept tested: MLOps, Pipelines, and Automation (GPMLE-4.1)

Question 4 of 10

Objective GPMLE-2.4 Data Preparation and Feature Management

A recommendation system uses product tables, user event streams, support text, and images as input. What should a machine learning engineer consider about this input design?

Concept tested: Data Preparation and Feature Management (GPMLE-2.4)

Question 5 of 10

Objective GPMLE-1.5 Problem Framing and Use Case Selection

A support organization needs a Gemini-based assistant that uses private workflow data and calls internal systems under enterprise controls. Why is a custom Vertex AI workflow appropriate?

Concept tested: Problem Framing and Use Case Selection (GPMLE-1.5)

Question 6 of 10

Objective GPMLE-3.2 Model Development and Evaluation

A pricing-risk classifier has significant costs associated with both false positive and false negative predictions. Which pair of metrics best reflects this tradeoff?

Concept tested: Model Development and Evaluation (GPMLE-3.2)

Question 7 of 10

Objective GPMLE-5.3 Serving, Scaling, and Monitoring

A media model owner needs alerts when live prediction inputs drift away from the training baseline. Which Vertex AI capability is relevant?

Concept tested: Serving, Scaling, and Monitoring (GPMLE-5.3)

Question 8 of 10

Objective GPMLE-4.4 MLOps, Pipelines, and Automation

An insurance pipeline must enforce data-prep, training, evaluation, and deployment dependencies on every run. Which Vertex AI approach addresses this?

Concept tested: MLOps, Pipelines, and Automation (GPMLE-4.4)

Question 9 of 10

Objective GPMLE-2.3 Data Preparation and Feature Management

A claims data scientist needs a managed notebook environment for exploration, code review, and experiments close to Vertex AI resources. Which environment fits?

Concept tested: Data Preparation and Feature Management (GPMLE-2.3)

Question 10 of 10

Objective GPMLE-1.2 Problem Framing and Use Case Selection

A legal recommendation model improves offline loss, but users still ignore the suggestions. What should the team change before wider rollout?

Concept tested: Problem Framing and Use Case Selection (GPMLE-1.2)

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Question 1 A demand-forecasting model needs to predict revenue for each store and week. Which modeling task is most suitable for this scenario?

Answer choices

A. Regression
B. Binary classification
C. Image segmentation
D. Document translation

Correct answer

Regression

Regression predicts a continuous numeric value, not a class label. The model should be evaluated with numeric-error metrics that match the business cost of being wrong.

Wrong-answer review

B. Binary classification: Binary classification predicts one of two classes, such as yes/no, rather than a numeric amount.
C. Image segmentation: Image segmentation predicts pixel-level regions in images and does not apply to tabular revenue forecasting.
D. Document translation: Document translation changes text from one language to another; it does not predict numeric revenue.

Objective/domain: Model Development and Evaluation (GPMLE-3.1)

Question 2 An energy team needs to score millions of meter records overnight and write the results back to storage. Which prediction mode should the engineer choose?

A. Online inference for each user click
B. Batch prediction
C. Manual one-row prediction requests
D. Feature attribution analysis only

Batch prediction

Objective/domain: Serving, Scaling, and Monitoring (GPMLE-5.2)

Question 3 A supply-chain model build requires a managed workflow that automates the entire process, from data preparation and training to model evaluation and deployment. Which Vertex AI capability should the engineer utilize to orchestrate these steps and ensure repeatability?

A. Manual notebook execution for every run
B. Track only experiment metrics without orchestrating workflow steps
C. Vertex AI Pipelines
D. Deploy a model endpoint without workflow orchestration

Vertex AI Pipelines

Objective/domain: MLOps, Pipelines, and Automation (GPMLE-4.1)

Source: Introduction to Vertex AI Pipelines

Question 4 A recommendation system uses product tables, user event streams, support text, and images as input. What should a machine learning engineer consider about this input design?

A. Use only the structured tables and discard documents
B. Recognize the mix of structured and unstructured inputs
C. Assume unstructured data cannot support ML workloads
D. Treat image, text, and tabular features as identical schemas

Recognize the mix of structured and unstructured inputs

Objective/domain: Data Preparation and Feature Management (GPMLE-2.4)

Source: Professional ML Engineer certification

Question 5 A support organization needs a Gemini-based assistant that uses private workflow data and calls internal systems under enterprise controls. Why is a custom Vertex AI workflow appropriate?

A. Build a custom Vertex AI workflow with private data and controls
B. Use a generic public chatbot with no private context
C. Assume Gemini cannot process text
D. Choose a custom workflow only because it is always cheaper

Build a custom Vertex AI workflow with private data and controls

Objective/domain: Problem Framing and Use Case Selection (GPMLE-1.5)

Source: Overview of Vertex AI

Question 6 A pricing-risk classifier has significant costs associated with both false positive and false negative predictions. Which pair of metrics best reflects this tradeoff?

A. Mean squared error and R-squared
B. Precision and recall
C. Silhouette score and inertia
D. BLEU and ROUGE only

Precision and recall

Objective/domain: Model Development and Evaluation (GPMLE-3.2)

Question 7 A media model owner needs alerts when live prediction inputs drift away from the training baseline. Which Vertex AI capability is relevant?

A. Use Workbench notebooks to inspect old training data
B. Compare experiment metrics without monitoring live prediction data
C. Vertex AI Model Monitoring
D. Update a model registry alias as the drift detector

Vertex AI Model Monitoring

Objective/domain: Serving, Scaling, and Monitoring (GPMLE-5.3)

Source: Introduction to Vertex AI Model Monitoring

Question 8 An insurance pipeline must enforce data-prep, training, evaluation, and deployment dependencies on every run. Which Vertex AI approach addresses this?

A. Run every step manually from the console
B. Use Vertex AI Pipelines for explicit dependencies and repeatable orchestration
C. Track feature attribution instead of workflow steps
D. Use a serving endpoint as the pipeline scheduler

Use Vertex AI Pipelines for explicit dependencies and repeatable orchestration

Objective/domain: MLOps, Pipelines, and Automation (GPMLE-4.4)

Question 9 A claims data scientist needs a managed notebook environment for exploration, code review, and experiments close to Vertex AI resources. Which environment fits?

A. Vertex AI Workbench
B. Use Vertex AI Experiments to compare completed training runs
C. Use Vertex AI Pipelines to orchestrate production workflows
D. Use Model Registry to manage released model versions

Vertex AI Workbench

Objective/domain: Data Preparation and Feature Management (GPMLE-2.3)

Source: Introduction to Vertex AI Workbench

Question 10 A legal recommendation model improves offline loss, but users still ignore the suggestions. What should the team change before wider rollout?

A. Optimize the same offline loss without changing the objective
B. Revert to alphabetical sorting because ML metrics cannot help
C. Add more data without changing the misaligned metric
D. Use a behavior-aligned metric and validate with an A/B test

Use a behavior-aligned metric and validate with an A/B test

Objective/domain: Problem Framing and Use Case Selection (GPMLE-1.2)

Source: Introduction to Machine Learning | Google Cloud

Google ML Engineer Practice Test

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Practice with explanations that teach.

A demand-forecasting model needs to predict revenue for each store and week. Which modeling task is most suitable for this scenario?

An energy team needs to score millions of meter records overnight and write the results back to storage. Which prediction mode should the engineer choose?

A supply-chain model build requires a managed workflow that automates the entire process, from data preparation and training to model evaluation and deployment. Which Vertex AI capability should the engineer utilize to orchestrate these steps and ensure repeatability?

A recommendation system uses product tables, user event streams, support text, and images as input. What should a machine learning engineer consider about this input design?

A support organization needs a Gemini-based assistant that uses private workflow data and calls internal systems under enterprise controls. Why is a custom Vertex AI workflow appropriate?

A pricing-risk classifier has significant costs associated with both false positive and false negative predictions. Which pair of metrics best reflects this tradeoff?

A media model owner needs alerts when live prediction inputs drift away from the training baseline. Which Vertex AI capability is relevant?

An insurance pipeline must enforce data-prep, training, evaluation, and deployment dependencies on every run. Which Vertex AI approach addresses this?

A claims data scientist needs a managed notebook environment for exploration, code review, and experiments close to Vertex AI resources. Which environment fits?

A legal recommendation model improves offline loss, but users still ignore the suggestions. What should the team change before wider rollout?

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