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Updated: Oct 21, 2026
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Google Professional-Machine-Learning-Engineer Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Machine Learning Engineer Certification Exam |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Exam Price: | $200 USD (plus tax where applicable) |
| Available Languages: | Japanese, English |
| Real Exam Qty: | 50-60 |
| Passing Score: | Not officially published, approximately 70% |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Google Cloud Professional Cloud Architect Google Cloud Professional Data Engineer |
| Exam Format: | Multiple select, Multiple choice |
| Exam Duration: | 120 minutes |
| Recommended Training: | Official Exam Guide Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online-proctored remote exam or onsite-proctored exam at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Topic 2: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs |
| Topic 3: Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
| Topic 4: Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates |
| Topic 5: Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization - Select appropriate model architectures and frameworks |
| Topic 6: Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Monitor data quality and pipeline health |
The Professional-Machine-Learning-Engineer Details Every Candidate Should Check
As of the latest information, the Professional-Machine-Learning-Engineer exam requires a passing score of Not officially published, approximately 70% and a registration fee of $200 USD (plus tax where applicable). Google may revise either figure, so confirm both on the official site before scheduling.
Google lists these prerequisites for the Google Professional Machine Learning Engineer: No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions.
Check the current requirements on the official certification page before booking.
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Smart preparation means current materials, verified answers, and a schedule that fits real life. The Professional-Machine-Learning-Engineer bank is written by a team of Google experts and certified trainers, and our colleagues check it continuously for updates so accuracy never drifts. You practice in your spare time — no classes required — with expert-verified answers across the Google Professional Machine Learning Engineer objectives. Before purchase, a free demo is downloadable for reference; after purchase, updates arrive by email for 365 days and 24/7 customer assisting handles any question. That combination is the smart way.
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Per current exam information, the Professional-Machine-Learning-Engineer exam contains 50-60 questions within a 120 minutes-minute limit. Practicing under those exact conditions removes the format shock on exam day.
The Google Professional Machine Learning Engineer blueprint centers on these main domains:
- Train and deploy models (20%)
- Automate and orchestrate ML pipelines (18%)
- Scale prototypes into AI models (18%)
The full official outline continues with further domains; the question bank covers them all.
Google points candidates to these official training resources:
- Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path
- Official Exam Guide
For office workers short on class time, disciplined spare-time practice with a current bank covers the same syllabus at a fraction of the cost.
Use the official registration channels below:
Pick your test center or online session and book ahead — a fixed date focuses preparation like nothing else.
Google Professional Machine Learning Engineer Sample Questions:
You are part of a team of ML engineers building a new churn prediction model using PyTorch.
Your team's raw data is stored in BigQuery. For the initial prototyping phase, your team needs a collaborative, cloud-based environment that meets the following criteria:
- It must provide access to GPU accelerators.
- It must allow seamless authentication to BigQuery without manual
management of service account keys.
- It must be pre-configured with common ML frameworks.
You need to choose the most secure solution with the least setup overhead for your team to begin model development and experimentation. What should you do?
- A. Instruct team members to use the free version of Agent Platform Colab Enterprise. Use the Agent Platform SDK for Python to load the data into a shared Agent Platform Colab Enterprise notebook. Have each member perform analysis in the shared notebook.
- B. Package the PyTorch model code in a Docker container, and upload it to Artifact Registry. Use Agent Platform Serverless Training to run a custom training job that runs on a GPU-enabled machine type. Instruct the team to analyze the training logs to iterate on the model.
- C. Configure a JupyterHub server on a cluster of Compute Engine instances. Install the necessary GPU drivers and other required libraries on each instance. Instruct each team member to create and download a service account key for BigQuery access.
- D. Create Agent Platform Workbench instances for your team members. Select instance types with attached GPUs and the preinstalled PyTorch kernel. Use the BigQuery client library to access the data, and use the instances' managed identity for authentication.
Correct Answer: D 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
You are an ML researcher and are evaluating multiple deep learning-based model architectures and hyperparameter configurations. You need to implement a robust solution to track the progress of each model iteration, visualize key metrics, gain insights into model internals, and optimize training performance.
You want your solution to have the most efficient and powerful approach to compare the models and have the strongest visualization abilities. How should you bull this solution?
- A. Use Vertex AI TensorBoard for in-depth visualization and analysis, and use BigQuery for experiment tracking and analysis.
- B. Use Vertex AI Experiments for tracking iterations and comparison, and use BigQuery and Looker Studio for visualization and analysis of the training metrics and model architecture.
- C. Use Vertex AI Experiments for tracking iterations and comparison, and use Vertex AI TensorBoard for visualization and analysis of the training metrics and model architecture.
- D. Use Vertex AI TensorBoard for visualizing training progress and model behavior, and use Vertex AI Feature Store to stove and manage experiment data for analysis and reproducibility.
Correct Answer: C 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Run a hyperparameter tuning job on AI Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
- B. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
- C. Apply a dropout parameter of 0.2, and decrease the learning rate by a factor of 10.
- D. Run a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters.
Correct Answer: D 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?
- A. Compare the loss performance for each model on a held-out dataset.
- B. Compare the receiver operating characteristic (ROC) curve for each model using the What-If Tool.
- C. Compare the mean average precision across the models using the Continuous Evaluation feature.
- D. Compare the loss performance for each model on the validation data.
Correct Answer: C 🗳️
Explanation: Only visible for PrepAwayETE members. You can sign-up / login (it's free).
You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises, and the data science workloads are native to PySpark. Your team plans to migrate their data science workloads to Google Cloud. You need to build a proof of concept to migrate one data science job to Google Cloud. You want to propose a migration process that requires minimal cost and effort. What should you do first?
- A. Create a n2-standard-4 VM instance and install Java, Scala, and Apache Spark dependencies on it.
- B. Create a Standard (1 master, 3 workers) Dataproc cluster, and run a Vertex AI Workbench notebook instance on it.
- C. Create a Google Kubernetes Engine cluster with a basic node pool configuration, install Java, Scala, and Apache Spark dependencies on it.
- D. Create a Vertex AI Workbench notebook with instance type n2-standard-4.
Correct Answer: D 🗳️
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