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PDE Practice Question: A data engineer is designing a streaming pipeline…
A data engineer is designing a streaming pipeline with Cloud Pub/Sub and Cloud Dataflow. They need to guarantee at-least-once delivery and handle occasional duplicates. Which TWO configurations should they implement?
⚠ Common exam trap
Google Cloud often tests the misconception that windowing strategies (global or fixed) or execution engine features (Streaming Engine) can substitute for explicit delivery guarantees and idempotent sinks, when in fact they address entirely different concerns.
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
✓
Use idempotent sinks
Idempotent sinks (e.g., BigQuery with insertId, Cloud Storage with object generation numbers) allow the pipeline to safely process duplicate records without causing data corruption or double-counting. In a streaming pipeline with at-least-once semantics, duplicates are inevitable, and idempotent sinks ensure that repeated writes produce the same result as a single write, maintaining data consistency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use idempotent sinks
Why this is correct
Idempotent sinks allow safe duplicate writes, ensuring exactly-once effect despite duplicates.
- ✗
Use global windows with triggers
Why it's wrong here
Windowing is unrelated to delivery guarantees.
- ✗
Use fixed windows
Why it's wrong here
Fixed windows do not affect delivery semantics.
- ✓
Use at-least-once Pub/Sub subscription
Why this is correct
At-least-once subscription ensures messages are delivered, even if duplicated.
- ✗
Enable Dataflow Streaming Engine
Why it's wrong here
Streaming Engine improves performance but does not affect at-least-once or duplicate handling.
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