How many Select Two (Multi-Select) Questions questions are on this page?

This page has 20 Select Two (Multi-Select) Questions scenario questions for the PDE exam, each with detailed explanations and wrong-answer analysis.

How should I approach PDE scenario questions?

Read the full scenario before looking at the answer options. Identify the constraint or requirement in the scenario, then eliminate options that are generally true but wrong for this specific case. Scenario questions reward careful reading over pattern matching.

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Select Two (Multi-Select) Questions

Practise Google Professional Data 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.

Practice scenarios

Question 1mediummulti select

Select Two (Multi-Select) Questions

How to approach select two (multi-select) questions

Quick answer

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Practice scenarios

Which TWO actions are recommended to improve the reliability of a Cloud Dataflow streaming pipeline that processes event data from Pub/Sub?

A data engineering team is operationalizing a machine learning model for real-time fraud detection. The model must process transactions with sub-100ms latency and be highly available. Which TWO strategies should the team implement?

Which TWO are best practices for monitoring a deployed machine learning model in production on Vertex AI?

A company is migrating an on-premises Hadoop cluster to Google Cloud. They need to run existing Spark jobs with minimal modification. Which THREE strategies should they consider? (Choose THREE.)

A data engineer is designing a batch processing system using Cloud Dataproc. Which TWO practices improve performance and reduce costs? (Choose TWO.)

Which TWO statements are correct about designing a data pipeline using Cloud Dataflow for processing unbounded data?

Which THREE considerations are important when designing a data lake on Google Cloud using Cloud Storage?

Which THREE best practices should be followed when designing a Dataflow pipeline for real-time data processing?

Which TWO factors should be considered when choosing between Cloud Dataflow and Dataproc for a batch processing pipeline?

Which TWO actions can help reduce the latency of a Vertex AI endpoint serving a large neural network model?

Which TWO actions can reduce the cost of running a Dataproc cluster for a nightly batch job?

A data pipeline uses Cloud Pub/Sub to ingest events, then a Cloud Dataflow job writes to BigQuery. The Dataflow job is failing with 'deadline exceeded' errors. Which TWO actions can resolve this? (Choose TWO.)

A company is deploying a machine learning model for fraud detection. The model is trained using TensorFlow and will be served on Vertex AI Prediction. The team wants to implement model monitoring to detect prediction drift. Which TWO actions should they take? (Choose 2)

Which TWO statements about designing a data processing pipeline on Google Cloud are correct? (Choose 2.)

A data team uses Cloud Composer to orchestrate Airflow DAGs. They need to ensure that a downstream task runs only if at least two out of three upstream sensor tasks succeed. Which TWO configurations should they combine?

Which THREE factors should be considered when designing a Vertex AI Pipeline for continuous training?

Which THREE considerations are important when designing a batch prediction pipeline for a large dataset on Vertex AI?

Which TWO steps are required to deploy a custom scikit-learn model to Vertex AI for online predictions?

Which TWO approaches are recommended for handling late-arriving data in a streaming Dataflow pipeline?

Which TWO actions can help reduce prediction latency for a Vertex AI endpoint?