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PDE Practice Question: Which TWO are valid approaches to handle…
Which TWO are valid approaches to handle late-arriving data in a Cloud Dataflow streaming pipeline?
⚠ Common exam trap
Google Cloud often tests the misconception that processing time windows are a valid substitute for handling late data, but they fundamentally change the semantics from event-time to processing-time, which is not a proper solution for late-arriving events.
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 allowed lateness on the window
Setting allowed lateness on a window in Cloud Dataflow allows the pipeline to wait for late-arriving data within a specified duration after the watermark passes the window end. This is a standard mechanism to handle out-of-order or delayed events without discarding them, ensuring completeness of windowed aggregations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change to processing time windows instead of event time windows
Why it's wrong here
Processing time windows ignore event timestamps.
- ✓
Set allowed lateness on the window
Why this is correct
Allowed lateness tells the pipeline how long to wait for late data.
- ✓
Use a side input with a fixed window to join late data
Why this is correct
Side inputs can hold prior results to update them.
- ✗
Discard any events that arrive after the window closes
Why it's wrong here
Discarding data may lose important information.
- ✗
Use a trigger that fires every second
Why it's wrong here
Too frequent triggering increases computational cost.
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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.