A company is planning to migrate its on-premises Hadoop workloads to Google Cloud. They want to use managed services but also want to keep the ability to move back to on-premises if needed. They have data residency requirements that require data to stay in a specific country. What is the best approach?

Use Cloud Storage for data and Cloud Functions for processing.

Use BigQuery for all processing and storage, as it is fully managed.

Use Cloud Dataflow with Apache Beam, but store data in BigQuery.

Use Cloud Dataproc with open-source Spark and store data in Cloud Storage in the required region.

A media company has many data assets spread across Cloud Storage, BigQuery, and external sources. Analysts often cannot find the right datasets, and they are unsure about data quality. The company wants to improve data discovery and governance. What should they implement first?

A data lake on Cloud Storage with all data stored in one place.

A machine learning model to automatically tag data assets.

A set of data profiling jobs that run periodically and report quality issues.

A data catalog that captures metadata, lineage, and quality scores, integrated with BigQuery and Cloud Storage.

Which scenario best illustrates the use of data staging?

Streaming data directly from IoT devices to a real-time dashboard

Querying a data warehouse directly for a business report

Copying raw files from a source system to a temporary bucket before cleaning and loading them into a data warehouse

Creating a backup of a database to a separate region

A company is designing a data platform for a new product. They expect to start with a small amount of data but anticipate rapid growth. They also need to support both batch and streaming processing. They want to minimize operational overhead and avoid rearchitecting later. What is the best approach?

Use Cloud Functions for both batch and streaming processing.

Use Cloud Pub/Sub for ingestion, Cloud Storage for staging, and Dataflow for both batch and streaming processing.

Use BigQuery for both storage and processing, with scheduled queries.

Use Cloud Dataproc for both batch and streaming, with a persistent cluster.

What is the main goal of data discovery?

To physically move data to a new location

To delete unused data to save costs

To enable users to find and understand available data assets through search and metadata

Google CloudProfessional Data Engineer

Domain 1Objective 3

1.3 Designing for Flexibility and Portability PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 3)

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 11–15

11expert · hard
A company is planning to migrate its on-premises Hadoop workloads to Google Cloud. They want to use managed services but also want to keep the ability to move back to on-premises if needed. They have data residency requirements that require data to stay in a specific country. What is the best approach?
Select an answer first
12application · medium
A media company has many data assets spread across Cloud Storage, BigQuery, and external sources. Analysts often cannot find the right datasets, and they are unsure about data quality. The company wants to improve data discovery and governance. What should they implement first?
Select an answer first
13foundation · easy
Which scenario best illustrates the use of data staging?
Select an answer first
14expert · hard
A company is designing a data platform for a new product. They expect to start with a small amount of data but anticipate rapid growth. They also need to support both batch and streaming processing. They want to minimize operational overhead and avoid rearchitecting later. What is the best approach?
Select an answer first
15foundation · easy
What is the main goal of data discovery?
Select an answer first

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1.3 Designing for Flexibility and Portability PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 3)

Questions 11–15

A media company has many data assets spread across Cloud Storage, BigQuery, and external sources. Analysts often cannot find the right datasets, and they are unsure about data quality. The company wants to improve data discovery and governance. What should they implement first?

Which scenario best illustrates the use of data staging?

What is the main goal of data discovery?

Keep studying — Designing data processing systems