The answer is Dataflow retrying BigQuery streaming inserts after a timeout, with retries succeeding even though the original insert succeeded. This occurs because Dataflow uses at-least-once semantics for streaming pipelines; when a streaming insert into BigQuery times out due to network latency or transient errors, Dataflow automatically retries the operation. If the original insert actually completed but the acknowledgment was lost, the retry writes the same row again, creating a duplicate. On the Google Professional Data Engineer exam, this scenario tests your understanding of exactly-once versus at-least-once guarantees in streaming systems, and it often appears as a trap where candidates mistakenly blame windowing or Pub/Sub redelivery. The key insight is that BigQuery streaming inserts are not idempotent by default, so retries can produce duplicates. Memory tip: think of it as a “lost handshake” — the data arrived, but the confirmation didn’t, so the sender sends it again.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing 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 is building a real-time streaming pipeline using Pub/Sub and Dataflow to process clickstream data. The pipeline writes aggregated metrics to BigQuery every 10 seconds using a fixed window. During peak traffic, some windows produce duplicate rows in BigQuery. What is the most likely cause?
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
✓
Dataflow is retrying BigQuery streaming inserts after a timeout, and the retries succeed even though the original insert succeeded.
Option A is correct because Dataflow uses at-least-once semantics for streaming inserts into BigQuery. When a streaming insert times out, Dataflow retries the insert, and if the original insert actually succeeded but the acknowledgment was lost, the retry produces a duplicate row. This is a known behavior of BigQuery streaming inserts with retry logic.
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.
✓
Dataflow is retrying BigQuery streaming inserts after a timeout, and the retries succeed even though the original insert succeeded.
Why this is correct
This is a known scenario: BigQuery streaming inserts are not idempotent, and retries can lead to duplicates.
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 uses default triggers instead of after-watermark triggers.
Why it's wrong here
Trigger type does not cause duplicates.
✗
The fixed window duration is too short, causing overlapping windows.
Why it's wrong here
Fixed windows are non-overlapping.
✗
The pipeline is using too many Dataflow workers, causing load balancing issues.
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse trigger behavior (Option B) with the root cause of duplicates, not realizing that duplicates stem from retry semantics in the sink, not from windowing or parallelism.
Detailed technical explanation
How to think about this question
BigQuery streaming inserts have a timeout of typically 10 seconds; if no acknowledgment is received within that time, Dataflow retries the insert. The BigQuery API is idempotent only if the insertId parameter is set, but if the pipeline does not use insertId or uses a non-deterministic ID, retries can create duplicates. In practice, enabling exactly-once sinks or using a deduplication step (e.g., with a unique key) mitigates this issue.
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.
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Dataflow is retrying BigQuery streaming inserts after a timeout, and the retries succeed even though the original insert succeeded. — Option A is correct because Dataflow uses at-least-once semantics for streaming inserts into BigQuery. When a streaming insert times out, Dataflow retries the insert, and if the original insert actually succeeded but the acknowledgment was lost, the retry produces a duplicate row. This is a known behavior of BigQuery streaming inserts with retry logic.
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.
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Question Discussion
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