The answer is that late-arriving events cause the afterWatermark trigger to fire additional panes for the same window, creating duplicates. When you switch from afterProcessingTime to afterWatermark, the pipeline becomes dependent on the watermark to estimate event-time progress. If a late event arrives after the watermark has advanced, the afterWatermark trigger fires a new pane for that already-closed window, writing a duplicate row to BigQuery. The previous trigger, afterProcessingTime, fired solely on processing-time intervals and did not react to late data, so no duplicates occurred. On the Google Professional Data Engineer exam, this scenario tests your understanding of how watermark-based triggers handle out-of-order data and the specific behavior of late data in streaming pipelines. A common trap is assuming all triggers behave identically with late data, but afterWatermark explicitly allows late firings. Memory tip: “Watermark waits, then wakes for late takes” — after the watermark passes, it will still trigger for stragglers, causing duplicates if not handled with accumulation or discarding.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company runs a production Dataflow streaming pipeline that reads from Pub/Sub, groups events by customer ID, and writes to BigQuery. The pipeline uses global windows with triggers. After a recent code change, the pipeline started generating duplicate events in BigQuery for the same customer ID. The previous version did not have duplicates. The team reviews the code and sees that the trigger was changed from 'afterProcessingTime' to 'afterWatermark'. What is the most likely reason for duplicates?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Late-arriving events cause the afterWatermark trigger to fire additional panes for the same window
The change from `afterProcessingTime` to `afterWatermark` introduces a dependency on the watermark, which estimates event time progress. When late-arriving events (those with timestamps before the watermark) arrive after the watermark has advanced, the `afterWatermark` trigger fires an additional pane for the same window, causing duplicate writes to BigQuery. The previous trigger (`afterProcessingTime`) fired based on processing time, which does not react to late data in the same way, hence no duplicates.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
The afterProcessingTime trigger fired multiple times for the same window
Why it's wrong here
Processing time triggers do not cause duplicates based on late data.
✓
Late-arriving events cause the afterWatermark trigger to fire additional panes for the same window
Why this is correct
Watermark triggers can fire again for late data, producing duplicates if not deduplicated.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The pipeline is firing early and on-time panes for the same window
Why it's wrong here
Early and on-time firings are separate; duplicates only if late data triggers additional panes.
✗
The pipeline uses accumulation mode which accumulates results across firings
Why it's wrong here
Accumulation mode can cause duplicates, but the trigger change is the key factor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between processing-time and event-time triggers, and the trap here is that candidates assume `afterWatermark` is simply a 'one-time' trigger, overlooking that late-arriving data can cause additional firings.
Detailed technical explanation
How to think about this question
In Apache Beam (which underlies Dataflow), the `afterWatermark` trigger fires once when the watermark passes the end of the window, but if late data arrives, it can fire additional panes for that window. This is controlled by the `withAllowedLateness` setting; if allowed lateness is set, late events trigger a new pane, leading to duplicate writes unless deduplication logic (e.g., using insertId in BigQuery) is implemented. A real-world scenario is a streaming pipeline processing clickstream data where network delays cause events to arrive out of order; without proper deduplication, the same customer event can be written multiple times.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Late-arriving events cause the afterWatermark trigger to fire additional panes for the same window — The change from `afterProcessingTime` to `afterWatermark` introduces a dependency on the watermark, which estimates event time progress. When late-arriving events (those with timestamps before the watermark) arrive after the watermark has advanced, the `afterWatermark` trigger fires an additional pane for the same window, causing duplicate writes to BigQuery. The previous trigger (`afterProcessingTime`) fired based on processing time, which does not react to late data in the same way, hence no duplicates.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A financial services company needs to process high-frequency trading data with strict ordering guarantees. They use Pub/Sub with ordering keys and Dataflow. The pipeline occasionally produces out-of-order results. What is the most likely cause?
hard
A.Dataflow does not preserve order when using multiple workers
B.Dataflow uses at-least-once processing, which can reorder events
C.Pub/Sub does not guarantee message ordering
✓ D.The window trigger allows late data to be included after the main output
Why D: Option D is correct because Dataflow's default window trigger behavior allows late data to arrive after the main pane is emitted. When using Pub/Sub with ordering keys, late-arriving events (e.g., due to network delays or retries) can be assigned to the correct window but emitted in a separate pane, causing the final output to appear out-of-order relative to the event time. This is a known behavior when combining event-time windows with late data handling.
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Question Discussion
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