A marketing analytics team receives raw event data in Cloud Storage. They need to filter out bot traffic, map user IDs to customer IDs, and aggregate page views by campaign. They want to use a serverless, code-based approach with minimal infrastructure. What should they use?

A Dataflow pipeline using Apache Beam to filter, map, and aggregate the data, writing results to BigQuery.

Cloud Data Fusion with Wrangler to filter and map, then load to BigQuery.

A Dataproc cluster running Spark to perform the transformations.

BigQuery with SQL queries to filter, map, and aggregate.

A company has an existing batch pipeline that processes CSV files from Cloud Storage. They need to integrate a new streaming source that sends JSON messages to Pub/Sub. The streaming data has a different schema and may contain duplicates. They want to use the same processing logic for both batch and streaming. What is the best approach?

Create two separate pipelines: one for batch CSV and one for streaming JSON, then union the results in BigQuery.

Use a Dataflow pipeline with Apache Beam that can read from both Cloud Storage (batch) and Pub/Sub (streaming), with a unified schema and deduplication logic.

Use Cloud Data Fusion to create a batch pipeline for CSV and a streaming pipeline for Pub/Sub.

Use Dataproc with Spark to process both batch and streaming, but with separate jobs.

Google CloudProfessional Data Engineer

Domain 2Objective 2

2.2 Building the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 1)

Part of the Ingesting and processing the data domain, which accounts for ~25% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~13–20 in this domain), expect 4–7 from this objective — we provide 38 practice questions to prepare you well beyond it. (estimate)

38questions here

8free pages

10concepts

~25%of the exam

Questions 1–5

1foundation · easy
Which Google Cloud service is a fully managed real-time messaging service that allows applications to send and receive messages asynchronously?
Select an answer first
2foundation · easy
A data engineer wants to write a single pipeline that can process both batch and streaming data. Which Google Cloud service provides a unified programming model for this?
Select an answer first
3foundation · easy
Which BigQuery feature allows you to define a SQL query that runs automatically on a schedule and writes results to a destination table?
Select an answer first
4application · medium
A marketing analytics team receives raw event data in Cloud Storage. They need to filter out bot traffic, map user IDs to customer IDs, and aggregate page views by campaign. They want to use a serverless, code-based approach with minimal infrastructure. What should they use?
Select an answer first
5expert · hard
A company has an existing batch pipeline that processes CSV files from Cloud Storage. They need to integrate a new streaming source that sends JSON messages to Pub/Sub. The streaming data has a different schema and may contain duplicates. They want to use the same processing logic for both batch and streaming. What is the best approach?
Select an answer first

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2.2 Building the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 1)

Questions 1–5

Which Google Cloud service is a fully managed real-time messaging service that allows applications to send and receive messages asynchronously?

A data engineer wants to write a single pipeline that can process both batch and streaming data. Which Google Cloud service provides a unified programming model for this?

Which BigQuery feature allows you to define a SQL query that runs automatically on a schedule and writes results to a destination table?

A marketing analytics team receives raw event data in Cloud Storage. They need to filter out bot traffic, map user IDs to customer IDs, and aggregate page views by campaign. They want to use a serverless, code-based approach with minimal infrastructure. What should they use?

Keep studying — Ingesting and processing the data