PDE Designing Data Processing Systems Practice Question
A company wants to use Cloud Data Fusion for ETL pipelines. They need to integrate with custom transformations not available in the marketplace. What should they do?
⚠ Common exam trap
PDE often tests the extensibility of Cloud Data Fusion, and candidates might incorrectly assume that the Data Fusion Hub provides custom plugins or that other GCP services like Dataprep can be used interchangeably.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Write a custom plugin using the CDAP SDK and deploy it.
Cloud Data Fusion allows extending its capabilities by writing custom plugins using the CDAP SDK, which can then be deployed to the Data Fusion instance. This enables integration of transformations not available in the marketplace, providing full flexibility for custom ETL logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to Dataproc and write a Spark job.
Why it's wrong here
Dataproc runs Spark jobs, abandoning the Cloud Data Fusion pipeline entirely rather than extending it. Custom transformations belong in a Cloud Data Fusion plugin, uploaded to the Hub. Dataproc is correct when migrating existing Spark or Hadoop workloads needing cluster control.
- ✗
Use the Data Fusion Hub to download a custom plugin.
Why it's wrong here
Downloading from the Hub only retrieves published plugins; it cannot supply a transformation that does not yet exist. Custom logic must be built as a Cloud Data Fusion plugin and uploaded. The Hub is correct when a prebuilt plugin already meets the requirement.
- ✗
Use Dataprep to create the transformation.
Why it's wrong here
Dataprep is a separate, self-service data-preparation tool for visual exploration and cleansing; it does not extend Cloud Data Fusion pipelines with custom transformation logic. Building a Cloud Data Fusion plugin is the answer. Dataprep suits analysts wrangling datasets independently of ETL pipelines.
- ✓
Write a custom plugin using the CDAP SDK and deploy it.
Why this is correct
The CDAP SDK lets developers author custom transformations as plugins, package them, and deploy into Cloud Data Fusion, extending the pipeline beyond marketplace offerings. This satisfies the requirement for transformations unavailable in the marketplace, which built-in operators and existing plugins cannot supply.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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