PDE Ingesting and Processing the Data Practice Question
A logistics company streams vehicle telemetry into a Pub/Sub topic. Each message contains a vehicle ID and a timestamp, and the Dataflow pipeline computes per-vehicle distance using a stateful DoFn with a ValueState timer set to fire after 5 minutes of event-time inactivity. During a regional network outage, some vehicles stop sending data for 20 minutes and then resume with correctly ordered timestamps. After the outage, operators notice that some late-arriving records are being dropped before the stateful computation. Which pipeline setting should be adjusted to retain those records for processing?
⚠ Common exam trap
Watch out — candidates often confuse message-level delivery guarantees in Pub/Sub with event-time window semantics in Beam, where late data is discarded based on the watermark rather than on acknowledgement timing.
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 an allowed lateness on the windowing strategy that exceeds the 20-minute outage gap.
Late data handling is governed by allowed lateness on the window, not by Pub/Sub delivery settings or the timer domain. When the watermark advances past a window end after a 20-minute gap, records with earlier timestamps are dropped unless the window is kept alive. Setting allowed lateness beyond the outage gap lets the stateful computation include the resumed telemetry in the correct event-time window.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch the pipeline's windowing from event-time to processing-time windows.
Why it's wrong here
Processing-time windows group records by arrival time on the worker, which would misattribute telemetry during an outage and merge records from different real-world intervals. Late-arriving records would still not be aligned with the original event-time windows, so the drop problem persists while the distance calculation becomes less accurate.
- ✓
Set an allowed lateness on the windowing strategy that exceeds the 20-minute outage gap.
Why this is correct
Allowed lateness extends the window's lifetime beyond the watermark so records arriving after the watermark passes the window end are still processed instead of being dropped. A value greater than 20 minutes covers the outage gap. The stateful DoFn timers continue to fire, but late elements are routed into the still-open window and contribute to distance calculations.
- ✗
Increase the Pub/Sub subscription acknowledgement deadline so messages remain outstanding longer.
Why it's wrong here
The acknowledgement deadline governs how long Pub/Sub waits for an ack before redelivering a message; it does not affect how Dataflow classifies a record as late relative to the watermark. Messages that already arrived and were acked cannot be recovered by extending the deadline, so late records would still be discarded by the windowing logic.
- ✗
Change the stateful DoFn timer from event-time to processing-time so it fires only when data resumes.
Why it's wrong here
Processing-time timers fire based on the worker's clock rather than the data's event time, which would make the 5-minute inactivity rule dependent on when workers happen to run. That does not retain late records; it changes when downstream emission occurs and can prematurely close state while late telemetry is still in flight, worsening the loss.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.