Google Cloud · Professional
Google Cloud Professional Machine Learning Engineer
Architect, build, scale, and monitor ML solutions on Google Cloud.
Professional Machine Learning Engineer exam at a glance
| Exam code | — |
|---|---|
| Vendor | Google Cloud |
| Level | Professional |
| Domains | 6 |
| Published practice questions | 251 |
| Our mock exam | 50 questions in 120 minutes |
| Our practice pass target | 70% |
| Checked against the exam guide | July 6, 2026 |
| Official exam page | Google Cloud — Professional Machine Learning Engineer |
The mock exam length, time limit and pass target are our practice settings, not official exam scores or a guarantee of passing. Check the official Professional Machine Learning Engineer exam page for the current format, price and scoring before you book.
Professional Machine Learning Engineer exam domains
Weights show our practice allocation, built from the official exam guide; official blueprints may give ranges. Each domain links to its free sample questions (with explanations) and study guide preview once they are published.
Architecting low-code ML solutions
13%Develop ML models with BigQuery ML, AutoML, and pretrained APIs.
- BigQuery ML model types, training and prediction in SQL
- AutoML tabular/vision/text training and evaluation
- Pretrained APIs: Vision, Natural Language, Speech, Translation
- When to use low-code vs custom training
- Vertex AI Model Garden and foundation model selection
Free Architecting low-code ML solutions practice questions →Architecting low-code ML solutions study guide →
Collaborating within and across teams
14%Manage data exploration, feature engineering, and reproducible environments.
- Vertex AI Workbench and Colab Enterprise notebooks
- Feature engineering and Vertex AI Feature Store
- Data preprocessing with Dataflow, BigQuery, TFX components
- Experiment tracking with Vertex AI Experiments
- Source control and reproducibility for ML code
- Responsible AI: fairness, explainability basics
Free Collaborating within and across teams practice questions →Collaborating within and across teams study guide →
Scaling prototypes into ML models
18%Build, train, and tune models with Vertex AI custom training.
- Custom training jobs: containers, Python packages, GPUs/TPUs
- Distributed training strategies
- Hyperparameter tuning with Vertex AI Vizier
- Framework choices: TensorFlow, PyTorch, scikit-learn, XGBoost
- Handling imbalanced data, regularization, early stopping
- Checkpointing and retraining strategies
Free Scaling prototypes into ML models practice questions →Scaling prototypes into ML models study guide →
Serving and scaling models
20%Deploy models for online and batch prediction and scale serving infrastructure.
- Vertex AI endpoints: deployment, traffic splitting, autoscaling
- Online vs batch prediction trade-offs
- Private endpoints and serving security
- Model optimization: quantization, distillation basics
- Serving with GKE/Cloud Run vs Vertex AI
- A/B testing and canary rollout of models
Free Serving and scaling models practice questions →Serving and scaling models study guide →
Automating and orchestrating ML pipelines
22%Build CI/CD-enabled, automated ML pipelines.
- Vertex AI Pipelines (Kubeflow/TFX), components and DAGs
- Pipeline triggers and scheduling
- Model Registry and model versioning
- CI/CD for ML: testing, validation gates, deployment automation
- Metadata tracking and lineage
- Orchestration alternatives: Cloud Composer for ML workflows
Free Automating and orchestrating ML pipelines practice questions →Automating and orchestrating ML pipelines study guide →
Monitoring ML solutions
13%Monitor, troubleshoot, and maintain deployed ML solutions.
- Vertex AI Model Monitoring: skew and drift detection
- Prediction logging and alerting
- Explainable AI: feature attributions
- Troubleshooting training and serving failures
- Cost and performance monitoring of ML workloads
- Retraining triggers and continuous evaluation
Free Monitoring ML solutions practice questions →Monitoring ML solutions study guide →
Professional Machine Learning Engineer FAQ
How is the Professional Machine Learning Engineer practice assessment configured?
Our practice format targets 50 questions in 120 minutes. Availability depends on the published question bank and is shown on this course page. Official exam question counts and formats may differ.
What does the 70% practice target mean?
Check the provider's current exam guide for official scoring. A practice percentage is not an official scaled score. DataCertPrep uses 70% as an internal practice target, not a prediction or guarantee of passing.
How should I assess my progress in Professional Machine Learning Engineer?
Use practice accuracy to find weak topics. Repeating familiar questions can inflate your score. Check coverage of the current objectives, practise hands-on tasks, and assess yourself on unseen questions.
What is the best way to study Professional Machine Learning Engineer?
Read the available guides, work through practice explanations, use flashcards and complete practical exercises. Take a timed assessment when available. Check the provider's current exam guide before booking your exam.
Should I use Professional Machine Learning Engineer exam dumps?
No. "Dumps" are questions copied or recalled from the live exam. Certification exams are covered by a candidate agreement that prohibits sharing or using them, and breaking it can lead to a revoked certification or a ban from future exams. Dumps are also often wrong, and they go out of date when the exam blueprint changes. Memorising answers does not build the skills the exam tests. Study the official Google Cloud exam guide and practise with original questions that explain each answer, such as the free sample questions linked on this page.