PDE Designing Data Processing Systems Practice Question
Which Google Cloud service provides a visual interface for building ETL pipelines using a drag-and-drop design and includes pre-built transforms from a marketplace?
⚠ Common exam trap
The trap is confusing Dataprep with Data Fusion — both are visual, but Dataprep is for interactive data preparation while Data Fusion is the full ETL pipeline builder with a transform marketplace.
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
✓
Cloud Data Fusion
Cloud Data Fusion is Google Cloud's fully managed, code-free ETL/ELT service built on the open-source CDAP project, offering a graphical drag-and-drop pipeline designer and a marketplace of pre-built plugins and transformations. It lets data engineers build batch and streaming pipelines visually and deploy them to ephemeral Dataproc clusters. This matches the requirement for a visual interface with marketplace transforms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataproc
Why it's wrong here
Dataproc runs Apache Spark and Hadoop clusters, so pipelines are authored in code or notebooks rather than a drag-and-drop canvas with marketplace transforms. It is tempting because Dataproc does process ETL workloads at scale, but it is the execution engine, not the visual design layer the question specifies.
- ✓
Cloud Data Fusion
Why this is correct
Cloud Data Fusion provides a graphical drag-and-drop interface for building ETL pipelines, with a reusable plugin marketplace of pre-built transforms and connectors. This directly satisfies the stem's requirement for visual pipeline design plus marketplace transforms, unlike code-first services such as Dataflow or Dataproc.
- ✗
Dataprep
Why it's wrong here
Dataprep is a data-preparation service, not the pipeline builder described; Cloud Data Fusion provides the drag-and-drop ETL canvas with a reusable transform plugin marketplace. Dataprep's visual recipes clean and profile data for analysts, so it fits exploratory wrangling rather than orchestrated pipeline design and deployment.
- ✗
BigQuery
Why it's wrong here
BigQuery is a serverless analytics warehouse queried with SQL; it has no drag-and-drop ETL designer or transform marketplace. It is tempting because BigQuery executes transformations and integrates with pipeline tools, but it is the destination and query engine, not the visual authoring interface the question requires.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PDE question from scratch — 747 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.