PDE Ingesting and Processing the Data Practice Question
A company is building a real-time anomaly detection pipeline using Dataflow. Events are ingested from Pub/Sub, and the pipeline must compute a sliding window average every minute over a 1-hour window. Which TWO configurations are required for this pipeline? (Choose 2)
⚠ Common exam trap
The trap is assuming a FixedWindow can produce a rolling average — candidates who conflate 'window size' with 'update frequency' pick FixedWindow of 1 minute and miss that sliding windows require both a duration and a period.
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 the pipeline to use event time for watermarking.
Option A is correct because a sliding window over event data must be based on event time so that late-arriving events are assigned to the correct window and watermarks track the progress of event time rather than the wall-clock time at which elements are processed. Option B is correct because the requirement is a 1-hour window that is recomputed every minute, which is exactly a SlidingWindow with a duration of 1 hour and a period (slide) of 1 minute. Option C is incorrect because a FixedWindow of 1 minute produces independent non-overlapping 1-minute aggregates, not a 1-hour average refreshed each minute. Option D is incorrect because stateful processing with a custom timer is a lower-level mechanism and is not required when Apache Beam's built-in sliding windows already express the desired semantics. Option E is incorrect because processing-time watermarking ignores the event timestamps and would misassign late or out-of-order Pub/Sub messages, breaking the anomaly detection logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set the pipeline to use event time for watermarking.
Why this is correct
Event-time watermarking derives progress from the timestamp embedded in each Pub/Sub message rather than arrival time, so the one-hour sliding window aggregates correctly despite network delays and out-of-order events. Without it, window results would be non-deterministic and inaccurate.
- ✓
Use a Sliding window of 1 hour with a 1-minute slide.
Why this is correct
A Sliding window with a one-hour duration and one-minute period emits an updated average every minute, each computed over the preceding hour. This exactly matches the stem's requirement to compute a sliding window average every minute over a one-hour window.
- ✗
Use a Fixed window of 1 minute.
Why it's wrong here
A fixed window of one minute produces independent per-minute aggregates, not the required one-hour sliding average recomputed each minute. SlidingWindows with a one-hour duration and one-minute period is needed. Fixed windows suit periodic non-overlapping reporting, such as hourly billing totals.
- ✗
Use stateful processing with a custom timer.
Why it's wrong here
Custom state and timers are not required here because Dataflow's built-in `SlidingWindows` primitive is precisely designed to handle the specified 1-hour sliding window, computing averages efficiently. This option is tempting as anomaly detection often involves complex state, and custom timers *can* manage time-based logic. However, they are intended for highly bespoke, event-driven state management or non-standard aggregations that fall outside Dataflow's comprehensive windowing capabilities, such as sessionisation or complex pattern matching that cannot utilise standard windowing strategies.
- ✗
Set the pipeline to use processing time for watermarking.
Why it's wrong here
Processing-time watermarking ignores the event timestamps carried in Pub/Sub messages, so late or out-of-order events land in the wrong window and skew the average. Event-time watermarking with allowed lateness is required. Processing time suits pipelines where arrival order is the only meaningful ordering.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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