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Exam Review

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 Sections

Professional ML Engineer exam guide (June 1, 2026)

3+ yrs

Recommended Experience

Google Cloud certification page

14 days

First Retake Wait

Google Cloud Certification retake policy

Google's PMLE exam has 50-60 multiple-choice and multiple-select questions in 2 hours for $200, delivered online or at test centers through Pearson. The June 1, 2026 exam guide weights six sections: Architecting low-code AI solutions (~13%), Collaborating to manage data and models (~16%), Scaling prototypes into ML models (~21%), Serving and scaling models (~20%), Automating and orchestrating ML pipelines (~18%), and Monitoring AI solutions (~13%). Results are pass/fail only.

Sample GCP ML Engineer Practice Questions

Try these sample questions to review concepts for the GCP ML Engineer exam. Each question includes a detailed explanation. Start the interactive quiz above for the full 123+ 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 build, evaluate, productionize, and optimize AI solutions on Google Cloud. Google's exam guide dated June 1, 2026 uses Gemini Enterprise Agent Platform (formerly Vertex AI) names and covers conventional ML and gen AI, including BigQuery ML, AutoML, Model Garden, Gemini tuning, pipelines, serving, Model Armor, and Model Monitoring.

Exam sponsor: Google Cloud. The requirements and fees below concern the certification or admission exam, separate from our free practice resources.

Assessment

Variable-length assessment

Time Limit

2 hours

Passing Score

Not published (pass/fail only)

Exam / Certification Fees

$200

Exam sponsor website

Reported exam pass rate: Not published. Exam sponsor website

Fees, eligibility, and exam policies can change. Confirm them with the exam sponsor before applying or paying.

Our practice resources: topics covered

We aim to reflect publicly available exam outlines and topic information in our study resources. Coverage, format, and difficulty may differ from the actual exam, and we cannot guarantee that every detail is accurate or current. Confirm exam requirements, fees, and policies with the official exam sponsor.

~13%

Architecting low-code AI solutions

BigQuery ML, Agent Platform AutoML, Gemini tuning in BigQuery, Document AI, Vision, and Translation APIs, Model Garden, Imagen, Veo, and Gemini cost and availability

~16%

Collaborating within and across teams to manage data and models

Data exploration and preprocessing tools, Feature Store, PII protection, Workbench and Colab Enterprise, Experiments, ML Metadata, and LLM-as-a-judge evaluation

~21%

Scaling prototypes into ML models

Model type and product choice, interpretability, training data, custom training, Kubeflow on GKE, Tabular Workflows, troubleshooting, tuning, and accelerators

~20%

Serving and scaling models

Batch and online inference, Model Garden, Cloud Run, GKE, containers, Model Registry, A/B and canary rollouts, endpoints, and autoscaling

~18%

Automating and orchestrating ML pipelines

Data and model validation, Agent Platform Pipelines, Managed Service for Apache Airflow, Ray on Agent Platform, retraining policy, and CI/CD/CT with Cloud Build

~13%

Monitoring AI solutions

Model Armor, safety filters, bias monitoring, explainability, Model Monitoring, skew and drift, and gen AI monitoring

Preparing for the GCP ML Engineer Exam

What You Need to Know

  • Passing score: Not published (pass/fail only)
  • Assessment: Variable-length assessment
  • Time limit: 2 hours
  • Exam / certification fees: $200 Official sources

Using Our Practice Resources

  • Work through all 123 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

GCP ML Engineer: Suggested Study Strategy

1Learn the Vertex AI to Gemini Enterprise Agent Platform name map, because the June 2026 exam guide uses the new names
2Prioritize Scaling prototypes into ML models (~21%) and Serving and scaling models (~20%), the two largest sections
3Practice choosing between BigQuery ML, AutoML, AI APIs, Model Garden, and custom training for a given constraint
4Know gen AI operations: Gemini consumption options, Gen AI evals with LLM-as-a-judge, Model Armor, and safety filters
5Master Model Monitoring objectives: training-serving skew, data drift, concept drift, and feature attribution drift

Frequently Asked Questions

How many questions are on the GCP ML Engineer exam?

The Professional Machine Learning Engineer exam has 50-60 multiple-choice and multiple-select questions, and you have 2 hours to complete it.

What is the GCP ML Engineer exam fee?

The registration fee is $200 plus tax where applicable. The price is the same for online-proctored and test center delivery.

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

Google doesn't publish a passing score. Results are reported only as pass or fail, and candidates who fail can view a section-level score report in the certification portal.

Does the 2026 exam cover generative AI?

Yes. The June 1, 2026 exam guide includes Model Garden model selection, Gemini tuning (including from BigQuery), Imagen and Veo, Gemini cost and latency optimization, LLM-as-a-judge evaluation, Model Armor, and monitoring gen AI solutions.

Can I take the GCP ML Engineer exam remotely?

Yes. Google Cloud exams are delivered by Pearson, either online-proctored through OnVUE or at a test center. Pearson delivery began on March 2, 2026, replacing Kryterion.

What experience does Google recommend for this exam?

Google recommends 3+ years of industry experience, including at least 1 year designing and managing solutions on Google Cloud. The exam doesn't directly assess coding, but minimum Python and SQL proficiency helps you read code snippets.

What is the retake policy for the GCP ML Engineer exam?

You can attempt the exam up to 4 times in 2 years. After a failed attempt, wait 14 days; after a second failure, wait 60 days; after a third failure, wait 365 days. Each retake costs the full fee.