PDE Maintaining and Automating Data Workloads Practice Question
Which Dataflow feature allows you to package a pipeline into a reusable template that can be deployed with different parameters at runtime?
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
✓
Flex Templates
Dataflow Flex Templates allow you to containerize a pipeline and provide runtime parameters, enabling reusability across different environments or jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Dataproc
Why it's wrong here
Cloud Dataproc runs Spark and Hadoop clusters, so it executes pipelines rather than packaging them into parameterised templates. It is tempting because it is the natural home for Spark workloads, and would be correct when the requirement is running existing Spark or Hadoop jobs on managed clusters.
- ✗
Classic Templates
Why it's wrong here
Classic Templates package a pipeline as a linked-service-bound snapshot, so runtime parameter substitution is not supported; the stem requires deploy-time parameters. Classic Templates suit one-off migrations of existing ADF pipelines, where fixed connections are acceptable.
- ✗
Dataflow SQL
Why it's wrong here
Dataflow SQL lets you author pipelines using SQL statements, but it does not package them into parameterised, reusable templates. It is tempting because SQL is a familiar authoring route, and would be correct when you want to define a pipeline declaratively in SQL rather than reuse one across differing runtime inputs.
- ✓
Flex Templates
Why this is correct
Flex Templates package the pipeline as a Docker image plus a metadata file, allowing runtime parameterisation when launched via the gcloud CLI, REST API or Cloud Scheduler. This satisfies the requirement to reuse one pipeline definition across differing runtime parameters.
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