PDE Maintaining and Automating Data Workloads Practice Question
An engineer needs to create a reusable Dataflow pipeline that can be executed with different parameters without modifying code. Which Dataflow feature should they use?
⚠ Common exam trap
PDE often tests the difference between Classic and Flex Templates, where candidates incorrectly choose Classic Templates for dynamic parameterization.
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
✓
Dataflow Flex Templates
Dataflow Flex Templates allow packaging a pipeline as a Docker image with a metadata file, enabling reuse with different parameters at runtime without code changes. They support dynamic parameters and are the recommended approach for reusable pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataflow Shuffle
Why it's wrong here
Dataflow Shuffle is a backend execution service that moves shuffle data to the Dataflow service rather than worker disks; it exposes no parameterisation or reuse mechanism. It is tempting because it improves pipeline performance, but the requirement concerns passing runtime parameters without editing code, which Shuffle does not address.
- ✓
Dataflow Flex Templates
Why this is correct
Flex Templates package the pipeline as a Docker image with a metadata specification, so the same template runs repeatedly with different runtime parameters and no code changes. This satisfies the stem's reusability and parameterisation constraint.
- ✗
Dataflow SQL
Why it's wrong here
Dataflow SQL lets you author pipelines using SQL syntax over BigQuery datasets, but it does not itself provide a parameterised, reusable template invoked repeatedly with runtime values. It is tempting because SQL queries can contain variables, yet those are query parameters, not pipeline-level runtime parameters passed at job submission.
- ✗
Dataflow Classic Templates
Why it's wrong here
Classic Templates are legacy and cannot be flexibly parameterised at runtime without code changes; they are deprecated in favour of Flex Templates. It is tempting because templates package pipelines for reuse, but Flex Templates accept runtime parameters, which this scenario explicitly requires.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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