PDE Designing Data Processing Systems Practice Question

You are designing a Dataflow pipeline for processing real-time clickstream data. The pipeline must group events into 30-second windows and handle late data up to 5 minutes. You want to output partial results every 10 seconds for low-latency monitoring. Which THREE configurations should you use? (Choose three.)

⚠ Common exam trap

Candidates might focus only on windowing and lateness, forgetting that early firing triggers are essential for periodic partial results as specified.

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 fixed windows of 30 seconds

Option C is correct because fixed (tumbling) 30-second windows partition the clickstream into non-overlapping 30-second intervals, which is the required grouping for this scenario. Option D is correct because setting allowed lateness to 5 minutes lets the pipeline retain window state and accept events that arrive up to 5 minutes after the window closes, matching the late-data requirement. Option E is correct because a trigger with early firings every 10 seconds emits speculative partial results before the window closes, providing the requested low-latency monitoring output. Option A is not appropriate because sliding windows with a 10-second period would create overlapping 30-second windows and duplicate events across windows, which is not the specified grouping. Option B is not appropriate because a trigger that fires only after the end of the window would produce results only at window closure and would not deliver the required 10-second partial outputs.

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 sliding windows of 30 seconds with a 10-second period
Why it's wrong here
Sliding windows of 30 seconds with a 10-second period produce overlapping panes, so events are counted in multiple windows and totals are duplicated. It is tempting because the 10-second period appears to match the latency requirement, and it would be correct for computing moving averages rather than distinct 30-second groupings.
✗
Use a trigger that fires after the end of the window
Why it's wrong here
A trigger firing only at window end emits nothing during the 30-second window, so the required 10-second partial results never appear. It is tempting because end-of-window firing is the default behaviour, and it would be correct when complete, non-speculative results are wanted and latency is unimportant.
✓
Use fixed windows of 30 seconds
Why this is correct
Fixed 30-second windows partition the clickstream into non-overlapping intervals matching the grouping requirement. Combined with allowed lateness and early triggering, they satisfy the windowing constraint while the other settings handle late data and partial results.
✓
Set allowed lateness to 5 minutes
Why this is correct
Allowed lateness of 5 minutes lets the window retain its state and accept events arriving after the watermark passes, satisfying the stem's requirement to handle late data up to 5 minutes. Without it, late clickstream events would be dropped rather than incorporated into the 30-second window results.
✓
Use a trigger with early firings every 10 seconds
Why this is correct
Early firings emit speculative panes every 10 seconds before the 30-second window closes, satisfying the low-latency monitoring requirement without waiting for watermark completion. Late data arriving within the 5-minute allowance is still incorporated into subsequent firings, so partial results refresh rather than being discarded.

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PDE Designing Data Processing Systems Practice Question

Why each option matters

Option-by-option breakdown

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