Google Cloud Professional Machine Learning Engineer Exam Prep
The Google Cloud Professional Machine Learning Engineer exam validates the ability to build, evaluate, productionize, optimize, monitor, and orchestrate ML and generative AI solutions on Google Cloud. The shared ExamPal PMLE release pack includes 801 premium questions and a 40-question free practice exam mapped to the official exam guide. Candidates should be comfortable reasoning about BigQuery ML, Vertex AI, Model Garden, AutoML, data and feature management, model training, serving, pipeline orchestration, monitoring, and responsible AI practices.
Exam Details
Exam Overview
Administered by
Google Cloud Certifications
Exam Format
50-60 multiple-choice and multiple-select questions, 120 minutes
Passing Score
Not publicly disclosed
Exam Fee
$200 plus tax
Prerequisite
Google Cloud ML engineering experience recommended
Topics Covered
ExamPal covers all major topics tested on the Google Cloud Professional Machine Learning Engineer exam. Our questions are grounded in official study materials.
Low-code AI Solutions
BigQuery ML, ML APIs, foundation models, AutoML, and Vertex AI Agent Builder use cases.
Data and Model Collaboration
Organization-wide data preprocessing, notebooks, feature management, experiments, and generative AI evaluation.
Model Training and Scaling
Model architecture, custom training, distributed training, hyperparameter tuning, and accelerator selection.
Serving and Scaling Models
Batch and online inference, model registry, A/B testing, endpoints, and production performance tuning.
ML Pipeline Orchestration
End-to-end pipelines, validation, retraining automation, metadata, lineage, and CI/CD deployment.
Monitoring AI Solutions
Responsible AI, explainability, drift, skew, continuous evaluation, and troubleshooting.
Exam Blueprint
What the Google Cloud Professional Machine Learning Engineer Exam Tests
The exam is divided into 6 domains. Here is what each domain covers and how much weight it carries on the test.
Domain 1: Architecting low-code AI solutions
13% of examThis section covers building AI and ML solutions with low-code and managed Google Cloud services. It includes BigQuery ML, ML APIs and foundation models, and AutoML workflows for training and debugging models.
- Developing ML models by using BigQuery ML
- Building AI solutions by using ML APIs or foundation models
- Training models by using AutoML
Key references: Google Cloud official exam guide · ExamPal shared topic tree
Domain 2: Collaborating within and across teams to manage data and models
14% of examThis section covers organization-wide data exploration, preprocessing, notebook-based prototyping, and ML experimentation. It emphasizes Google Cloud data services, Vertex AI tooling, and evaluation of generative AI solutions.
- Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Hadoop)
- Model prototyping using Jupyter notebooks
- Tracking and running ML experiments
Key references: Google Cloud official exam guide · ExamPal shared topic tree
Domain 3: Scaling prototypes into ML models
18% of examThis section covers model design, training at scale, distributed training, hyperparameter tuning, troubleshooting, and hardware selection. It also includes fine-tuning foundation models and choosing compute and accelerator options.
- Building models
- Training models
- Choosing appropriate hardware for training
Key references: Google Cloud official exam guide · ExamPal shared topic tree
Domain 4: Serving and scaling models
20% of examThis section covers batch and online inference, model serving frameworks, model registry, A/B testing, and scaling online serving backends. It also includes hardware selection and production tuning for latency, memory, throughput, and performance.
- Serving models
- Scaling online model serving
Key references: Google Cloud official exam guide · ExamPal shared topic tree
Domain 5: Automating and orchestrating ML pipelines
22% of examThis section covers end-to-end pipeline development, retraining automation, and metadata tracking and auditing. It includes validation, orchestration frameworks, hybrid and multicloud strategies, CI/CD deployment, and lineage/versioning.
- Developing end-to-end ML pipelines
- Automating model retraining
- Tracking and auditing metadata
Key references: Google Cloud official exam guide · ExamPal shared topic tree
Domain 6: Monitoring AI solutions
13% of examThis section covers AI risk identification, responsible AI practices, explainability, continuous evaluation, skew and drift monitoring, and troubleshooting. It emphasizes secure AI systems and ongoing monitoring of model performance and errors.
- Identifying risks to AI solutions
- Monitoring, testing, and troubleshooting AI solutions
Key references: Google Cloud official exam guide · ExamPal shared topic tree
Why study with ExamPal
Everything you need to prepare for and pass the Google Cloud Professional Machine Learning Engineer exam, in one app.
- 801 PMLE scenario-based premium questions
- Free 40-question interactive practice exam
- Official Google Cloud domain coverage
- Terminology glossary for Vertex AI, BigQuery ML, MLOps, and model serving
- Spaced repetition for weak domains
- Mistakes notebook to retry missed ML engineering scenarios
Google Cloud Professional Machine Learning Engineer Exam — Common Questions
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