Validates skills in building, deploying, automating and monitoring machine learning and generative AI solutions on Google Cloud.
The exam tests how you build, scale, serve, automate and monitor ML and generative AI solutions on Google Cloud. Learn where BigQuery ML, AutoML, custom training and Model Garden each fit, practise the MLOps flow on Agent Platform, then drill scenario questions that ask for the most Google Cloud native answer.
Example scenario: A retailer wants weekly demand forecasts from sales data already in BigQuery, with no ML engineers on the team. Which approach would you suggest first?
Example scenario: Two teams train similar churn models and cannot tell which features and data versions produced the best one. What would you put in place?
Example scenario: A large image model trains too slowly on one GPU and the dataset keeps growing. What options would you compare to speed it up?
Example scenario: A new fraud model looks better offline, but the team is nervous about switching all traffic. How would you roll it out?
Example scenario: A model's accuracy drops each quarter as customer behaviour changes. How would you automate retraining and safe redeployment?
Example scenario: A support chatbot sometimes repeats customer account numbers from its context. What controls would you add?
Professional Machine Learning Engineer exam guide · Guide as of June 1, 2026
Choose the one correct answer from the options.
A team's model sees different feature calculations in training than in production serving. What is the best way to keep them consistent?
Compute and serve the features from a shared feature store. The same definitions are then used for training and serving, which avoids training-serving skew.
Choose all the correct answers from the options; two or more answers are correct.
A deployed model's accuracy has fallen as customer behaviour changed. Which TWO issues should the team look for?
Data drift and concept drift. Both mean production data no longer matches what the model learned, and Model Monitoring can flag them.
Create a CM Connect account, choose the exam and delivery method, then pick a Pearson VUE time and pay. Your CM Connect legal name must match your ID exactly.
Read the June 2026 guide and try the sample questions, then follow one course with labs. Make sure your materials use Agent Platform names and cover the newer generative AI topics.
Most candidates spend 60–120 hours. Plan for the low end if you already run ML on Google Cloud; add time if you are new to Google Cloud or to ML.
Use Google's learning path or another course updated for the June 2026 guide. Older books and courses still help with ML fundamentals.
Best official pathProfessional Machine Learning Engineer Certification path
Best for practice examsGCP Professional ML Engineer Certification Course
Best for production MLPreparing for Google Cloud Certification - Machine Learning Engineer Professional CertificateLearn the decision points. For each scenario, decide between prompt engineering, RAG and fine-tuning, and between BigQuery ML, AutoML and custom training, before you look at the options.
Start with Google's sample questions. Many question banks still use older Vertex AI names, so check that a set covers the June 2026 topics before relying on it.
Professional Machine Learning Engineer Sample QuestionsGCP Professional ML Engineer Certification Course
Official Google Professional Machine Learning Engineer ExamsChoose the checklist for your test centre or online appointment.
Reschedule or cancel at least 24 hours ahead to avoid a late fee. After a fail, wait 14 days, then 60 days, then a year; you get up to four attempts in two years.
A course creator answering a candidate said the Vertex AI to Agent Platform change is mostly naming, but the update added topics such as choosing between prompting, RAG and tuning, Model Armor, LLM evaluation methods and Agent Search versus Vector Search. Another candidate found most question banks had not caught up.
Candidates say metrics, feature engineering and BigQuery ML are still heavily tested. A passer who already held three other Google Cloud certifications found only 10 to 15% overlap and rated this the hardest of them.
A passer noticed the intended answer was often the managed Google service, and that phrases such as "no operational overhead" pointed to specific options. They recommend working out why each sample answer is right or wrong.
A career changer who passed after two and a half weeks of full-time study found the certification alone was not enough and started building end-to-end ML projects.