A startup is building a data platform. They expect to grow rapidly and may need to change their data processing framework in the future. They also want to avoid vendor lock-in. They are considering using Cloud Dataflow, but they are concerned about portability. What is the best way to design for flexibility?

Use Cloud Functions for all processing, as they are simple and serverless.

Use Cloud Dataproc with Spark, as Spark is open-source and portable.

Use Dataflow templates and rely on Google Cloud's managed service.

Write pipelines using Apache Beam, which can run on Dataflow or other runners like Spark or Flink.

What is the primary goal of designing data and applications for portability?

To ensure the system can run in multiple environments with minimal changes

To ensure all data is stored in a single location

To reduce the need for data encryption

To maximize the use of proprietary features of a single cloud provider

A government agency has a large data lake with many datasets. Employees often do not know what data is available or how to access it. They want to enable users to search for data by keywords, tags, and descriptions. What should they implement?

A data catalog with a search interface that indexes metadata and tags.

A machine learning model that predicts which data users need.

A data profiling tool that runs on all datasets.

A data warehouse that consolidates all data into one place.

Which practice is essential for maintaining data residency compliance in a data processing system?

Restricting data movement to approved geographic locations

Storing all backups in a single global location

Using the same encryption key for all data

Allowing users to export data to any location

Google CloudProfessional Data Engineer

Domain 1Objective 3

1.3 Designing for Flexibility and Portability PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 2)

Part of the Designing data processing systems domain, which accounts for ~22% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~11–18 in this domain), expect 3–5 from this objective — we provide 30 practice questions to prepare you well beyond it. (estimate)

30questions here

6free pages

8concepts

~22%of the exam

Questions 6–10

6expert · hard
A startup is building a data platform. They expect to grow rapidly and may need to change their data processing framework in the future. They also want to avoid vendor lock-in. They are considering using Cloud Dataflow, but they are concerned about portability. What is the best way to design for flexibility?
Select an answer first
7foundation · easy
What is the primary goal of designing data and applications for portability?
Select an answer first
8foundation · easy
A data engineer is examining a new dataset to understand its structure, quality, and content before integrating it into a data warehouse. Which activity is this?
Select an answer first
9application · medium
A government agency has a large data lake with many datasets. Employees often do not know what data is available or how to access it. They want to enable users to search for data by keywords, tags, and descriptions. What should they implement?
Select an answer first
10foundation · easy
Which practice is essential for maintaining data residency compliance in a data processing system?
Select an answer first

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1.3 Designing for Flexibility and Portability PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 2)

Questions 6–10

What is the primary goal of designing data and applications for portability?

A data engineer is examining a new dataset to understand its structure, quality, and content before integrating it into a data warehouse. Which activity is this?

A government agency has a large data lake with many datasets. Employees often do not know what data is available or how to access it. They want to enable users to search for data by keywords, tags, and descriptions. What should they implement?

Which practice is essential for maintaining data residency compliance in a data processing system?

Keep studying — Designing data processing systems