Google Professional Machine Learning Engineer Exam Practice Questions, practice question pack
GOOGLE · Professional Machine Learning Engineer

Google Professional Machine Learning Engineer Exam Practice Questions

339 questionsPDF by emailUpdated September 2026

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Three of the 339 questions in this pack

Question 1

You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

  1. 1 = Dataflow, 2 = AI Platform, 3 = BigQuery
  2. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
  3. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
  4. 1 = BigQuery, 2 = AI Platform, 3 = Cloud Storage
Show answer and explanation

Correct answer: A. 1 = Dataflow, 2 = AI Platform, 3 = BigQuery

For real-time anomaly detection on sensor data, Dataflow provides scalable stream processing from Pub/Sub, AI Platform handles the ML model inference, and BigQuery stores results for analytics and visualization. This is the standard Google Cloud pattern for real-time ML pipelines where Dataflow acts as the processing engine, AI Platform serves the model, and BigQuery provides the data warehouse for downstream analytics.

Why the other options are wrong

  • B. DataProc is for batch processing and not optimal for real-time streaming; Bigtable is a NoSQL database not suited for analytics and visualization.
  • C. BigQuery should be the destination for analytics, not the processor; Cloud Functions lacks the scalability needed for continuous ML inference on high-volume sensor data.
  • D. BigQuery is not a processing engine; Cloud Storage is not suitable for serving ML models in real-time scenarios.

Question 2

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

  1. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
  2. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station. 2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.
  3. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.
  4. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.
Show answer and explanation

Correct answer: C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.

passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map. Since users confirm their presence and shuttle station in advance, this is a deterministic optimization problem with known constraints, not a prediction problem. The optimal solution uses route optimization algorithms to find the shortest route that satisfies capacity constraints and includes only confirmed stops. This approach is more efficient and interpretable than building ML models when ground truth data (confirmed attendance) is already available.

Why the other options are wrong

  • A. Regression predicts continuous values (number of passengers) when binary decisions (stop/no stop) are needed, and adds unnecessary complexity.
  • B. While classification could work, this ignores the optimization aspect and fails to minimize distance and maximize efficiency given known confirmed reservations.
  • D. Reinforcement learning is overly complex and inappropriate when the problem can be solved deterministically with known, confirmed attendance data and capacity constraints.

Question 3

You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

  1. Use the class distribution to generate 10% positive examples.
  2. Use a convolutional neural network with max pooling and softmax activation.
  3. Downsample the data with upweighting to create a sample with 10% positive examples.
  4. Remove negative examples until the numbers of positive and negative examples are equal.
Show answer and explanation

Correct answer: C. Downsample the data with upweighting to create a sample with 10% positive examples.

sample with 10% positive examples. With under 1% positive examples, the recommended Google practice is to downsample the majority class and then upweight the retained majority examples by the same factor. Downsampling to roughly 10% positives lets the model see far more of the rare failure signal and converge faster, while upweighting keeps the model calibrated to the true class distribution. This preserves all of the positive examples and the statistical meaning of the original data.

Why the other options are wrong

  • A. Generating extra positive examples from the class distribution fabricates failure records that do not reflect real sensor behavior and leaves the model calibrated to synthetic data.
  • B. A convolutional network with max pooling and softmax is an architecture choice for image-style inputs and does nothing to fix a skewed label distribution.
  • D. Discarding negative examples until the classes are equal throws away most of the majority data without any upweighting, so the model loses information and its predicted probabilities no longer match production conditions.

See all 10 free questions Get the full pack, US$39

339 practice questions for Google Cloud Professional Machine Learning Engineer, with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 339 questions mapped to the Professional Machine Learning Engineer exam objectives
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A Professional Machine Learning Engineer attempt costs US$200. This pack is US$39, paid once.

Try 10 questions free before you buy.

Last updated September 2026 · 339 questions

What makes the Professional Machine Learning Engineer hard

The Professional Machine Learning Engineer certification covers the full machine learning lifecycle on Google Cloud, from architecting low code AI solutions and collaborating across teams to manage data and models, through scaling prototypes into production models, serving and scaling them, automating and orchestrating pipelines, and monitoring AI solutions once they are live. The exam rewards practical, production grade ML judgement rather than algorithm theory.

It runs to 50 to 60 questions in 2 hours. No coding is assessed directly, but candidates are expected to read Python and SQL snippets.

This pack puts 339 practice questions in one place, mapped across every area of the blueprint, so the question style is familiar before exam day.

About the exam

The Professional Machine Learning Engineer certification is a widely recognised Google Cloud credential for engineers who take machine learning models from design into production. It proves the ability to build, deploy and manage ML systems on Google Cloud, and it has become a leading credential in the generative AI era. Google recommends 3+ years of industry experience including 1+ year on Google Cloud. Valid for 2 years.

Exam domains

  • Architect low-code AI solutions
  • Collaborate within and across teams to manage data and models
  • Scale prototypes into ML models
  • Serve and scale models
  • Automate and orchestrate ML pipelines
  • Monitor AI solutions

Google does not publish percentage weightings for these sections.

50 to 60 questions, 2 hours, multiple choice and multiple select, US$200 per attempt, available in English and Japanese, online proctored or test centre, valid 2 years.

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Questions before you buy

What do I get when I buy the Google Professional Machine Learning Engineer pack?

339 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

How quickly do I receive it?

Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Is there a free sample?

Yes. Ten questions from this pack, with answers and explanations, are free on this page and as a PDF, so you can judge the quality before you pay.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

Can I share it with colleagues?

Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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