Career upgrade: Learn practical AI skills for better jobs and higher pay.
Level up
PracticeVideosBlogFlashcardsEspañol
All Practice Exams

100+ Free GCP ML Engineer Practice Questions

Pass your Google Cloud Professional Machine Learning Engineer exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately
Not published Pass Rate
100+ Questions
100% Free
1 / 100
Question 1
Score: 0/0

You want to evaluate a generative AI model's responses for safety, factual grounding, and helpfulness. Which Vertex AI service provides automated evaluation of these dimensions?

A
B
C
D
to track
Same family resources

Explore More Google Cloud Certifications

Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.

Google Associate Cloud Engineer

Practice Questions1 blog

Google Professional Cloud Architect

Practice Questions1 blog

Google Cloud Digital Leader

Practice Questions1 blog

GCP Data Engineer

Practice Questions

Google Professional Data Engineer

Practice Questions

Google Professional DevOps Engineer

Practice Questions

More From This Family

Videos and articles for deeper review.

ArticleFREE Cloud Digital Leader Exam Guide 2026: $99, 90 Min, Pass First Try16 min readArticleFREE Google Associate Cloud Engineer Exam Guide 20265 min readArticleGoogle Cloud Architect Certification 2026: FREE Guide17 min read
2026 Statistics

Key Facts: GCP ML Engineer Exam

50-60

Questions

Google Cloud certification page

2 hrs

Exam Time

Google Cloud certification page

$200

Exam Fee

Google Cloud certification page

6

Exam Domains

Professional ML Engineer exam guide

3+ yrs

Recommended Experience

Google Cloud certification page

14 days

Retake Wait Period

Google Cloud exam policies

Google's current PMLE exam covers 50-60 questions in 2 hours at $200. The 2026 version includes generative AI content covering Model Garden and Vertex AI Agent Builder. Six domains are tested: Architecting Low-Code AI Solutions (~12%), Collaborating to Manage Data and Models (~16%), Scaling Prototypes (~18%), Serving and Scaling Models (~19%), Automating and Orchestrating ML Pipelines (~22%), and Monitoring AI Solutions (~13%). Delivered via Pearson VUE online or at testing centers.

Sample GCP ML Engineer Practice Questions

Try these sample questions to test your GCP ML Engineer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1A retail company wants to build a product recommendation engine using their existing BigQuery sales data. They have limited ML expertise on their team. Which GCP approach best fits this scenario?
A.Train a custom TensorFlow model on Vertex AI Training
B.Use BigQuery ML to create a matrix factorization model
C.Deploy a pre-trained model from Model Garden
D.Build a custom pipeline with Kubeflow on GKE
Explanation: BigQuery ML allows teams with limited ML expertise to build recommendation models directly in BigQuery using SQL. Matrix factorization is the standard BigQuery ML approach for recommendation systems, and it avoids the need to move data out of BigQuery or manage ML infrastructure.
2Your team needs to quickly build a document classification system for internal support tickets. The dataset contains 10,000 labeled examples across 15 categories. Which Vertex AI feature provides the fastest path to a production-ready model?
A.Vertex AI AutoML for text classification
B.Vertex AI custom training with a BERT model
C.Vertex AI Feature Store for ticket embeddings
D.Vertex AI Matching Engine for nearest neighbor lookup
Explanation: Vertex AI AutoML for text classification is the low-code solution that automatically trains, evaluates, and deploys a text classification model from labeled data. With 10,000 labeled examples across 15 categories, AutoML has sufficient data to produce a high-quality classifier with minimal ML expertise required.
3A data science team stores features in multiple BigQuery tables and Cloud Storage buckets. Different models reuse the same features but compute them independently, leading to training-serving skew. What should you implement to ensure feature consistency?
A.Create materialized views in BigQuery for each feature
B.Use Vertex AI Feature Store as a centralized feature repository
C.Write Cloud Functions to synchronize feature values hourly
D.Store all features in a single denormalized BigQuery table
Explanation: Vertex AI Feature Store provides a centralized, managed repository for ML features that ensures consistency between training and serving. It eliminates training-serving skew by allowing both training pipelines and online prediction to retrieve features from the same source of truth.
4You are designing an ML pipeline where multiple teams contribute datasets stored in different GCP projects. You need to track data lineage across these projects and ensure reproducibility. Which service should you use?
A.Cloud Data Catalog for metadata tagging
B.Vertex ML Metadata for tracking artifacts and lineage
C.Cloud Logging for audit trails
D.BigQuery INFORMATION_SCHEMA for dataset tracking
Explanation: Vertex ML Metadata is purpose-built for tracking ML artifacts, executions, and lineage across the ML lifecycle. It records relationships between datasets, models, and pipeline runs, enabling reproducibility across teams and GCP projects.
5Your team is developing a fraud detection model. The dataset has 99.5% legitimate transactions and 0.5% fraudulent ones. Which technique should you prioritize to handle this class imbalance?
A.Increase the training data volume by collecting more legitimate transaction examples
B.Use oversampling of the minority class combined with appropriate evaluation metrics like AUPRC
C.Reduce the model complexity to prevent overfitting on the majority class
D.Remove features that are correlated with the majority class
Explanation: For highly imbalanced fraud detection, oversampling the minority class (e.g., SMOTE) helps the model learn fraudulent patterns. Using area under the precision-recall curve (AUPRC) rather than accuracy is critical because accuracy would be misleadingly high (99.5%) even if the model never detected fraud.
6You need to train a deep learning model on a dataset that does not fit into the memory of a single GPU. The model architecture itself fits in a single GPU's memory. What is the most appropriate distributed training strategy?
A.Model parallelism to split the model across GPUs
B.Data parallelism to distribute data batches across multiple GPUs
C.Pipeline parallelism to split layers across GPUs
D.Asynchronous parameter server training
Explanation: Data parallelism distributes different data batches across multiple GPUs while each GPU holds a complete copy of the model. Since the model fits in a single GPU's memory but the dataset is too large, data parallelism is the correct strategy as it scales training throughput without requiring model splitting.
7Which Vertex AI feature allows you to run multiple training experiments simultaneously, track hyperparameters and metrics, and compare results across runs?
A.Vertex AI Pipelines
B.Vertex AI Experiments
C.Vertex AI Model Registry
D.Vertex AI TensorBoard
Explanation: Vertex AI Experiments is designed specifically for organizing, tracking, and comparing ML experiments. It logs hyperparameters, metrics, and artifacts for each run, enabling systematic comparison across multiple training configurations.
8A healthcare company wants to use a pre-trained foundation model to analyze medical images but needs to fine-tune it on their proprietary radiology dataset without sending data outside their VPC. Which approach should they use?
A.Use Vertex AI Model Garden and fine-tune within a VPC Service Controls perimeter
B.Export the model weights and train on an on-premises GPU cluster
C.Use a third-party model API with data anonymization
D.Deploy the model on Cloud Run and retrain with public datasets
Explanation: Vertex AI Model Garden provides access to pre-trained foundation models that can be fine-tuned within a VPC Service Controls perimeter. This ensures the proprietary radiology data never leaves the organization's security boundary while leveraging Google Cloud's managed training infrastructure.
9You are preparing a tabular dataset for ML training that contains both numerical and categorical features. The numerical features have vastly different scales, and the categorical features have high cardinality. Which preprocessing combination is most appropriate?
A.Min-max scaling for numerical features and one-hot encoding for all categorical features
B.Standardization for numerical features and feature hashing for high-cardinality categoricals
C.Log transformation for all features and label encoding for categoricals
D.No preprocessing needed if using tree-based models
Explanation: Standardization (z-score normalization) handles different numerical scales by centering features around zero with unit variance. Feature hashing efficiently handles high-cardinality categorical features by mapping them to a fixed-size vector without requiring a full vocabulary, avoiding the dimensionality explosion of one-hot encoding.
10Your model is deployed on a Vertex AI endpoint and receives variable traffic throughout the day, from 10 requests per second at night to 1,000 requests per second during peak hours. How should you configure the endpoint to optimize cost and latency?
A.Manually scale the number of replicas based on a fixed schedule
B.Configure autoscaling with minimum and maximum replica counts based on CPU utilization
C.Deploy the model to a single high-memory machine to handle peak traffic
D.Use batch prediction instead of online prediction
Explanation: Vertex AI endpoint autoscaling automatically adjusts the number of replicas based on traffic metrics like CPU utilization. Setting minimum and maximum replica counts ensures the endpoint maintains low latency during peak hours while scaling down to reduce costs during low-traffic periods.

About the GCP ML Engineer Exam

The Google Cloud Professional Machine Learning Engineer certification validates your ability to design, build, deploy, and optimize ML and AI solutions on Google Cloud. The 2026 exam now covers generative AI including Vertex AI Agent Builder, Model Garden, and foundation model fine-tuning alongside conventional ML topics like MLOps pipelines, model serving, and monitoring.

Questions

60 scored questions

Time Limit

2 hours

Passing Score

Not published (~70% estimated)

Exam Fee

$200 (Google Cloud)

GCP ML Engineer Exam Content Outline

~12%

Architecting Low-Code AI Solutions

AutoML, BigQuery ML, pre-trained APIs, Model Garden, Vertex AI Agent Builder, and RAG

~16%

Collaborating to Manage Data and Models

Feature Store, ML Metadata, experiment tracking, model governance, data privacy, and team collaboration

~18%

Scaling Prototypes into ML Models

Model training, distributed training, hyperparameter tuning, feature engineering, and evaluation metrics

~19%

Serving and Scaling Models

Vertex AI Endpoints, batch prediction, autoscaling, traffic splitting, and model optimization

~22%

Automating and Orchestrating ML Pipelines

Vertex AI Pipelines, TFX, Cloud Composer, CI/CD for ML, and data validation

~13%

Monitoring AI Solutions

Model monitoring, drift detection, Explainable AI, responsible AI, and fairness evaluation

How to Pass the GCP ML Engineer Exam

What You Need to Know

  • Passing score: Not published (~70% estimated)
  • Exam length: 60 questions
  • Time limit: 2 hours
  • Exam fee: $200

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

GCP ML Engineer Study Tips from Top Performers

1Focus on Vertex AI Pipelines and MLOps — the largest domain at 22% weighting
2Practice with the Google Cloud Free Tier to get hands-on Vertex AI experience
3Study generative AI integration using Model Garden and Vertex AI Agent Builder
4Master model monitoring concepts: data drift, prediction drift, and feature attribution
5Review TFX components and Kubeflow Pipelines SDK for pipeline orchestration

Frequently Asked Questions

How many questions are on the GCP ML Engineer exam?

The exam contains 50-60 multiple choice and multiple select questions, to be completed within 2 hours.

What is the GCP ML Engineer exam fee?

The registration fee is $200 USD plus applicable tax. This applies to both online-proctored and testing center sessions.

What score do I need to pass the GCP ML Engineer exam?

Google does not publish an exact passing score. Based on candidate reports, it is estimated to be around 70% correct answers.

Does the 2026 exam cover generative AI?

Yes. The current exam version includes generative AI tasks such as building solutions with Model Garden, Vertex AI Agent Builder, and evaluating generative AI solutions.

Can I take the GCP ML Engineer exam remotely?

Yes. Since February 2026, exams are delivered through Pearson VUE for both online-proctored and testing center options.

What experience does Google recommend for this exam?

Google recommends 3+ years of industry experience including 1+ year designing and managing ML solutions using Google Cloud. Proficiency in Python and SQL is expected.