PDE Maintaining and Automating Data Workloads Practice Question
You need to deploy a reusable Dataflow pipeline that can be executed with different parameters from Cloud Composer. Which TWO components should you use? (Choose 2)
⚠ Common exam trap
PDE often tests the distinction between Flex Templates and Classic Templates, as candidates may choose Classic Templates for reusability but overlook the need for runtime parameterization and the specific operator for Cloud Composer integration.
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 Template
Option B (Dataflow Flex Template) is correct because Flex Templates package the pipeline as a Docker image plus a template spec file, allowing the same pipeline to be reused and launched with different runtime parameters (such as input/output locations) via the templates launch API. Option C (Cloud Composer with DataflowStartFlexTemplateOperator) is correct because this operator is purpose-built to submit a Flex Template job from an Airflow/Composer DAG, passing the required parameters and letting Composer orchestrate the pipeline execution. Option A (Direct runner) is incorrect because it runs the pipeline locally for testing rather than deploying a reusable job on Dataflow. Option D (Dataflow Classic Template) is not the best fit here since Classic Templates are less flexible for parameterization and dependency packaging compared with Flex Templates. Option E (Cloud Scheduler) is incorrect because it only triggers jobs on a schedule and does not deploy or parameterize a Dataflow pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Direct runner
Why it's wrong here
The Direct runner executes pipelines locally for testing, not as a managed service Cloud Composer can invoke with runtime parameters. It is tempting because it runs Apache Beam code without a Dataflow service, but Composer requires a template or job launched remotely, which the Direct runner cannot provide.
- ✓
Dataflow Flex Template
Why this is correct
A Flex Template packages the pipeline as a Docker image with a metadata parameter spec, so the same artefact runs repeatedly with different runtime parameters. This satisfies the reusability constraint, unlike classic templates, which require rebuilding or redeploying the pipeline for parameter changes.
- ✓
Cloud Composer with DataflowStartFlexTemplateOperator
Why this is correct
DataflowStartFlexTemplateOperator launches a Flex Template job from Cloud Composer, passing runtime parameters per task instance. Combined with a Flex Template, it satisfies the orchestration and parameterisation requirement, unlike operators that only monitor existing jobs or submit non-templated pipelines.
- ✗
Dataflow Classic Template
Why it's wrong here
Dataflow Classic Templates are legacy and do not support the flexible runtime parameter passing that Cloud Composer requires for reusable pipelines. They are tempting because templates do package pipelines for repeated execution, and would be correct for simple batch jobs launched without dynamic, caller-supplied parameters.
- ✗
Cloud Scheduler
Why it's wrong here
Cloud Scheduler triggers jobs on a cron timetable; it cannot parameterise or orchestrate a Dataflow pipeline invoked from Cloud Composer. It is tempting because it schedules recurring workloads, but Composer already supplies scheduling and parameter passing through its own DAG operators, making Scheduler redundant here.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.