PDE Maintaining and Automating Data Workloads Practice Question
You need to create a reusable Dataflow pipeline for transforming CSV files in Cloud Storage into Avro files in another bucket. The pipeline should be configurable via runtime parameters (e.g., input and output paths). Which approach should you use?
⚠ Common exam trap
A common pitfall is choosing Dataflow Classic Templates because they seem simpler, but they lack support for custom Docker images and offer limited parameterization. The requirement for reusable, configurable pipelines with custom transformations (CSV to Avro) and runtime parameters calls for Flex Templates.
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
✓
Create a Dataflow Flex Template with a Docker image and parameterized metadata.
Dataflow Flex Templates allow you to package your pipeline code and dependencies into a Docker image, and define parameterized metadata (e.g., input and output paths) that are exposed as runtime parameters. This approach provides full customization of the execution environment and supports reusable, configurable pipelines for transforming CSV to Avro across different Cloud Storage buckets.
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 Functions triggered by Cloud Storage events.
Why it's wrong here
Cloud Functions are for lightweight event-driven processing, not for complex data transformations at scale.
- ✗
Create a Dataflow Classic Template with the pipeline code and parameters.
Why it's wrong here
Classic Templates are deprecated and less flexible for custom parameters; Flex Templates are the modern approach.
- ✗
Use Cloud Run Jobs to run the transformation as a container.
Why it's wrong here
Cloud Run Jobs are for short-lived batch jobs, not large-scale Dataflow pipelines.
- ✓
Create a Dataflow Flex Template with a Docker image and parameterized metadata.
Why this is correct
Flex Templates allow full customization and runtime parameters, making the pipeline reusable.
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