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PDE Practice Question: A data engineer needs to design a batch…
A data engineer needs to design a batch processing pipeline using Cloud Data Fusion. The pipeline should read data from Cloud Storage, perform transformations (join, filter, aggregate), and write to BigQuery. What is the most efficient way to handle the transformations?
⚠ Common exam trap
Google Cloud often tests the misconception that Cloud Data Fusion is only a visual tool and that transformations must be coded manually in Spark or SQL, when in fact Wrangler generates optimized Spark code under the hood and integrates seamlessly with Dataproc for execution.
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
✓
Use Data Fusion Wrangler to visually design the transformations and then run the pipeline on a Dataproc cluster.
Cloud Data Fusion Wrangler provides a visual, no-code interface for designing transformations (join, filter, aggregate) that are then compiled into an Apache Spark or MapReduce program and executed on a Dataproc cluster. This approach leverages Data Fusion's native integration with Dataproc for efficient, scalable batch processing without manual coding, while keeping the pipeline fully managed within the Data Fusion ecosystem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Data Fusion Wrangler to visually design the transformations and then run the pipeline on a Dataproc cluster.
Why this is correct
Wrangler provides a UI for transformations and Data Fusion executes them on Dataproc.
- ✗
Use SQL queries in BigQuery to perform the transformations after loading raw data into staging tables.
Why it's wrong here
This offloads transformations to BigQuery but Data Fusion can do it more efficiently within the pipeline.
- ✗
Use custom Python scripts in a Cloud Function triggered after the files land in Cloud Storage.
Why it's wrong here
Cloud Functions have execution time limits and are not designed for heavy ETL; Data Fusion is purpose-built.
- ✗
Use Apache Spark on Dataproc to code the transformations manually, bypassing Data Fusion.
Why it's wrong here
Manual Spark coding is maintenance-heavy; Data Fusion provides a higher-level abstraction.
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