A retail company collects clickstream events from its website into Pub/Sub. They need to build a pipeline that enriches each event with a customer segment (from a BigQuery table) and writes the result to BigQuery for near-real-time analytics. The team wants a single codebase that can also be reused for a nightly batch reprocessing job. What should they use?

A Dataflow pipeline written with the Apache Beam SDK, using a Pub/Sub source and a BigQuery sink, with the same pipeline code parameterized for batch and streaming.

A Cloud Composer DAG that triggers a Cloud Function to read from Pub/Sub and write to BigQuery, with a separate Cloud Function for nightly batch.

A Cloud Dataproc cluster running a Spark Streaming job that reads from Pub/Sub and writes to BigQuery, with a separate batch job for nightly reprocessing.

A Cloud Data Fusion pipeline with a Pub/Sub source and BigQuery sink, configured to run in real-time mode and also scheduled for batch.

A company has an existing Dataflow pipeline that processes JSON data from Cloud Storage. They need to integrate a new data source that provides XML files. The schema is different and contains some invalid records. They want to handle this with minimal changes to the existing pipeline. What should they do?

Modify the existing pipeline to accept both JSON and XML, but skip invalid records.

Add a new Dataflow pipeline branch that parses XML, cleans invalid records, and converts them to the existing JSON schema before joining.

Convert the XML files to JSON using a Cloud Function before they land in Cloud Storage.

Use Cloud Data Fusion to create a separate pipeline for XML and load it to BigQuery, then join in BigQuery.

A company has a Dataflow pipeline that reads from Cloud Storage, transforms data, and writes to BigQuery. They need to add a step that enriches the data by joining with a reference table stored in BigQuery. The reference table is updated hourly. What is the best way to implement this?

Use a BigQuery SQL query to join the data after loading.

Use a Beam side input that reads the reference table from BigQuery and refreshes it hourly.

Use a CoGroupByKey transform to join the main data with the reference table.

Load the reference table into the pipeline as a PCollection and use a regular join.

Google CloudProfessional Data Engineer

Domain 2Objective 2

2.2 Building the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 6)

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 26–30

26application · medium
A retail company collects clickstream events from its website into Pub/Sub. They need to build a pipeline that enriches each event with a customer segment (from a BigQuery table) and writes the result to BigQuery for near-real-time analytics. The team wants a single codebase that can also be reused for a nightly batch reprocessing job. What should they use?
Select an answer first
27foundation · easy
A data engineer with limited coding experience needs to build a data pipeline. Which Google Cloud service provides a visual, drag-and-drop interface for designing pipelines?
Select an answer first
28application · medium
A company has an existing Dataflow pipeline that processes JSON data from Cloud Storage. They need to integrate a new data source that provides XML files. The schema is different and contains some invalid records. They want to handle this with minimal changes to the existing pipeline. What should they do?
Select an answer first
29foundation · easy
Which Google Cloud service can be used to transfer data from another cloud provider (e.g., AWS S3) into Cloud Storage?
Select an answer first
30application · medium
A company has a Dataflow pipeline that reads from Cloud Storage, transforms data, and writes to BigQuery. They need to add a step that enriches the data by joining with a reference table stored in BigQuery. The reference table is updated hourly. What is the best way to implement this?
Select an answer first

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2.2 Building the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 6)

Questions 26–30

A data engineer with limited coding experience needs to build a data pipeline. Which Google Cloud service provides a visual, drag-and-drop interface for designing pipelines?

Which Google Cloud service can be used to transfer data from another cloud provider (e.g., AWS S3) into Cloud Storage?

A company has a Dataflow pipeline that reads from Cloud Storage, transforms data, and writes to BigQuery. They need to add a step that enriches the data by joining with a reference table stored in BigQuery. The reference table is updated hourly. What is the best way to implement this?

Keep studying — Ingesting and processing the data