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PDE Practice Question: Which TWO are best practices for managing a Cloud…
Which TWO are best practices for managing a Cloud Dataflow pipeline in production?
⚠ Common exam trap
Google Cloud often tests the misconception that disabling autoscaling or restarting pipelines is acceptable for cost control or simplicity, when in fact these actions violate production best practices for reliability and data integrity.
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
✓
Set up Cloud Monitoring alerts based on Dataflow job metrics
Option C is correct because Cloud Monitoring integrates with Dataflow job metrics (such as system lag, data watermark, and element count) so you can create alerting policies that detect stuck pipelines, backlog growth, or failures and respond before SLAs are breached. Option D is correct because Dataflow supports updating a running streaming pipeline via the --update and --transformNameMapping flags, which lets you change code or adjust transforms while preserving the pipeline's state and avoiding downtime. Option A is wrong because batch mode cannot process unbounded streaming data; streaming pipelines require streaming mode, and switching to batch would break the use case rather than reduce cost. Option B is wrong because disabling autoscaling removes Dataflow's ability to dynamically adjust worker count to workload, hurting performance and often increasing cost due to over- or under-provisioning. Option E is wrong because restarting a streaming pipeline loses in-flight state and causes downtime; the recommended approach is an update rather than a stop-and-restart.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Always use batch mode for streaming data to reduce cost
Why it's wrong here
Batch mode cannot ingest unbounded streaming data; it reads a bounded source and terminates, so a streaming pipeline switched to batch stops processing new events. It is tempting because batch mode does cost less, making it look like a valid cost-saving measure for continuous input.
- ✗
Disable autoscaling to keep compute costs predictable
Why it's wrong here
Disabling autoscaling fixes worker count, but executor lost errors and backlog growth follow when fixed capacity cannot absorb traffic spikes, and cost predictability comes from setting a maximum worker limit instead. It is tempting because unpredictable autoscaling spend is a genuine production concern.
- ✓
Set up Cloud Monitoring alerts based on Dataflow job metrics
Why this is correct
Cloud Monitoring alerts on Dataflow job metrics such as system lag, backlog and worker errors surface degradation before pipelines fail, enabling proactive intervention. This is a standard production operational practise for streaming and batch jobs.
- ✓
Use pipeline updates (update) to modify running streaming pipelines
Why this is correct
Streaming pipelines cannot be stopped and restarted without losing in-flight state, so the update option lets you replace the transform graph while preserving the running job's state and watermarks. This satisfies the production requirement of modifying a live streaming pipeline without downtime or data loss.
- ✗
Restart the pipeline when code changes are needed
Why it's wrong here
Restarting discards the pipeline's in-flight state and cannot apply code changes anyway; updates require a replacement job with updated code, letting Dataflow drain or snapshot state. Restarting suits recovering a stuck or unhealthy pipeline, not deploying new code.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.