PDE Maintaining and Automating Data Workloads Practice Question
A streaming Dataflow pipeline needs to be updated without draining the existing pipeline. Which update strategy should be used?
⚠ Common exam trap
The trap is assuming any update requires stopping or draining the pipeline; candidates pick the 'safe-sounding' drain option without realizing Dataflow's replace-job feature is specifically designed to avoid draining.
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
✓
Replace the job with a new job using the same pipeline name
Dataflow supports replacing a running streaming job with an updated one using the same pipeline name via the 'Replace job' (or update) mechanism, which performs an in-place upgrade that preserves the pipeline's state and does not require draining. This is the supported path for updating streaming pipelines without stopping data ingestion. Draining, cancelling, or stopping the job all interrupt processing and are not the recommended no-drain update strategy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Drain the pipeline first, then start a new one
Why it's wrong here
Draining stops ingestion and lets in-flight data finish before the job terminates, so the pipeline is unavailable and no in-place update occurs. It is tempting because draining is the safe route when changing the pipeline's fundamental shape, such as altering the windowing or key structure.
- ✓
Replace the job with a new job using the same pipeline name
Why this is correct
A replacement job cannot be updated in place, so the pipeline is relaunched under the same name, letting the new job start while the old one is cancelled. This avoids the drain step that would stop ingestion, satisfying the no-drain constraint.
- ✗
Use a different pipeline name and cancel the old one
Why it's wrong here
Launching under a new name and cancelling the old job abandons the existing pipeline's state and causes duplicate or lost processing, not an in-place update. It is tempting because it avoids compatibility checks, and it is correct when the new pipeline is genuinely incompatible with the old one.
- ✗
Stop the job, update the code, and restart
Why it's wrong here
Stopping the job discards the streaming pipeline's state and forces a cold restart, interrupting processing and losing exactly-once guarantees. It is tempting because it is the familiar batch-style deployment pattern, and it would be correct for a batch pipeline where no continuous state must be preserved.
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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.