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PDE Practice Question: A data engineering team needs to build a data…
A data engineering team needs to build a data integration pipeline that involves connecting to multiple sources, performing data transformations with visual editing, and then running custom machine learning algorithms. The team has both data analysts and data scientists. Which approach is most suitable?
⚠ Common exam trap
Google Cloud often tests the misconception that a single tool can handle both visual ETL and custom ML, leading candidates to choose Cloud Data Fusion alone (Option C) without realizing it lacks native support for running custom algorithms like Spark MLlib or TensorFlow.
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 Cloud Data Fusion for the initial ingestion and transformations, then export the data to Cloud Dataproc for the ML algorithms
It leverages Cloud Data Fusion's visual, no-code interface for data ingestion and transformation, which is ideal for data analysts, and then exports the prepared data to Cloud Dataproc, which provides native support for custom machine learning algorithms using Spark or Hadoop, meeting the data scientists' needs. This separation of concerns optimizes the pipeline for both user groups and avoids forcing all tasks into a single tool that may not excel at both visual ETL and custom ML.
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 Cloud Composer to orchestrate both Data Fusion and Dataproc
Why it's wrong here
While possible, this adds unnecessary complexity; Data Fusion can trigger Dataproc jobs directly.
- ✗
Use only Cloud Dataproc for all steps
Why it's wrong here
Dataproc requires coding for all transformations, lacking visual editing for analysts.
- ✗
Use only Cloud Data Fusion for all steps
Why it's wrong here
Data Fusion is great for integration but may have limitations for custom ML algorithms.
- ✓
Use Cloud Data Fusion for the initial ingestion and transformations, then export the data to Cloud Dataproc for the ML algorithms
Why this is correct
This leverages the strengths of both services: visual integration and custom ML.
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