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Select Two (Multi-Select) Questions

Practise Google Professional Machine Learning Engineer practice questions — original exam-style scenarios covering every exam domain, with detailed explanations, wrong-answer analysis, and common exam traps.

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Scenario guide

How to approach select two (multi-select) questions

Multi-select questions tell you to 'Choose TWO' or 'Choose THREE'. Getting partial credit is not a thing — you must select all correct answers with no incorrect ones. The stem always states how many to choose, so trust it. These questions require precision, not best-guess elimination.

Quick answer

Select Two (Multi-Select) Questions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

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Practice set

Practice scenarios

Question 1hardmulti select
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A financial services company has deployed a classification model on Vertex AI to detect fraudulent transactions. The model is monitored using Vertex AI Model Monitoring for skew and drift detection, and also logs predictions to BigQuery for analysis. After a month, the monitoring alerts show a significant drift in one feature (transaction_amount). Which TWO actions should the team take to diagnose and address this issue?

Question 2hardmulti select
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You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)

Question 3easymulti select
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Which TWO actions are appropriate when you detect that a production model's prediction distribution has shifted significantly from the training distribution?

Question 4hardmulti select
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A healthcare company uses AutoML Tables to predict patient readmission risk. The dataset contains 500,000 rows and 200 features, including patient demographics, lab results, and medical history. The model accuracy is lower than expected. The engineer wants to improve performance using low-code techniques. Which THREE actions are most effective? (Choose THREE.)

Question 5easymulti select
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A team has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?

Question 6easymulti select
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Which TWO actions are best practices when scaling a prototype ML model to production in Google Cloud?

Question 7hardmulti select
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Which TWO factors should you consider when choosing between BigQuery and Cloud Storage for storing training data? (Choose 2)

Question 8mediummulti select
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A company wants to build a low-code ML pipeline using Vertex AI Pipelines and BigQuery ML. They need to train, evaluate, and deploy a model. Which TWO statements are correct about the integration between Vertex AI Pipelines and BigQuery ML? (Choose TWO.)

Question 9hardmulti select
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A company trains a model using Vertex AI Training and then deploys it to Vertex AI Prediction. They notice that prediction requests fail with 'InvalidArgument: input tensor shape mismatch'. Which THREE are possible causes?

Question 10mediummulti select
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A company wants to reduce costs for serving a model on Vertex AI Prediction without sacrificing availability. Which THREE strategies should they consider?

Question 11hardmulti select
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An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)

Question 12mediummulti select
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Which TWO metrics should you monitor to detect data drift in a batch prediction pipeline?

Question 13hardmulti select
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Which THREE components should you include in a comprehensive model monitoring dashboard for a production ML system?

Question 14mediummulti select
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A team has trained a sentiment analysis model using PyTorch on Vertex AI Training. They now want to deploy it for online predictions with low latency. Which TWO actions should they take? (Choose 2)

Question 15easymulti select
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Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

Question 16mediummulti select
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Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

Question 17mediummulti select
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A company is deploying a model for online predictions on Vertex AI. They want to minimize latency while also handling traffic spikes. Which TWO configurations should they choose?

Question 18hardmulti select
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A company has a prototype ML model that predicts equipment failure. They want to deploy it to production using Vertex AI. The model must be retrained weekly with new data. They also need to monitor for data drift and model performance. Which THREE components should they include in their MLOps pipeline? (Choose 3)

Question 19hardmulti select
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Which TWO are best practices for implementing a low-code ML solution using Vertex AI AutoML? (Choose 2)

Question 20hardmulti select
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A machine learning team is deploying a model for real-time predictions using Vertex AI. They need to ensure that the deployment follows best practices for collaboration and governance. Which TWO actions should they take?

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