- A
The pipeline is using a global window with an early trigger, causing late data to be reprocessed.
Why wrong: Global windows with triggers do not cause duplicates if exactly-once is enabled; late data would be discarded or processed once.
- B
The Pub/Sub subscription is configured with at-least-once delivery, causing duplicate messages.
Why wrong: Pub/Sub uses at-least-once delivery, but Dataflow's exactly-once processing handles that by deduplicating at the pipeline level if using a stateful transform.
- C
The BigQuery table has a time-based partitioning column that is not aligned with the event timestamp.
Why wrong: Partitioning doesn't cause duplicates; it affects query performance and costs.
- D
The pipeline does not set the insertId parameter in the BigQuery streaming output.
BigQuery streaming inserts use insertId for deduplication. Without it, retried inserts may create duplicate rows.
Quick Answer
The answer is that the pipeline does not set the insertId parameter in the BigQuery streaming output. This is the most likely cause of duplicates because BigQuery’s streaming API relies on the insertId field to perform deduplication within its streaming buffer; without a unique identifier for each row, BigQuery cannot discard retried writes that occur when Dataflow workers restart, even if the pipeline itself uses exactly-once processing. On the Google Professional Data Engineer exam, this question tests your understanding of the boundary between Dataflow’s guarantees and BigQuery’s at-least-once streaming endpoint—a common trap is assuming that exactly-once processing in the pipeline eliminates all duplication downstream. Remember that insertId is the only mechanism for deduplication in BigQuery streaming, so always generate a deterministic, unique insertId (e.g., from a record hash or Pub/Sub message ID) in your Dataflow pipeline. Memory tip: “No ID, no dedup—BigQuery streams can’t clean up.”
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your Dataflow streaming pipeline is reading from Cloud Pub/Sub and writing to BigQuery. Users report occasional data duplication in the BigQuery table. You verify the pipeline uses exactly-once processing and idempotent writes. The Dataflow monitoring shows no errors, but the pipeline has occasional worker restarts. What is the most likely cause of the 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 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
The pipeline does not set the insertId parameter in the BigQuery streaming output.
Option D is correct because BigQuery's streaming API uses the `insertId` parameter to deduplicate records within the streaming buffer. Without a unique `insertId`, BigQuery cannot detect and discard duplicate inserts that may occur when Dataflow retries a write after a worker restart. Even with exactly-once processing in the pipeline, the BigQuery streaming endpoint itself is at-least-once, so the `insertId` is essential for deduplication.
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 pipeline is using a global window with an early trigger, causing late data to be reprocessed.
Why it's wrong here
Global windows with triggers do not cause duplicates if exactly-once is enabled; late data would be discarded or processed once.
- ✗
The Pub/Sub subscription is configured with at-least-once delivery, causing duplicate messages.
Why it's wrong here
Pub/Sub uses at-least-once delivery, but Dataflow's exactly-once processing handles that by deduplicating at the pipeline level if using a stateful transform.
- ✗
The BigQuery table has a time-based partitioning column that is not aligned with the event timestamp.
Why it's wrong here
Partitioning doesn't cause duplicates; it affects query performance and costs.
- ✓
The pipeline does not set the insertId parameter in the BigQuery streaming output.
Why this is correct
BigQuery streaming inserts use insertId for deduplication. Without it, retried inserts may create duplicate rows.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that exactly-once processing in the pipeline (Dataflow) automatically guarantees exactly-once delivery to the sink (BigQuery), ignoring that the sink itself may require explicit deduplication parameters like `insertId`.
Detailed technical explanation
How to think about this question
BigQuery streaming inserts use a best-effort deduplication mechanism based on the `insertId` field. If the same `insertId` is provided within the streaming buffer retention window (typically up to 1 hour), BigQuery will discard duplicate rows. Without it, each retry from Dataflow (triggered by worker restarts or network issues) creates a new row. In production, always generate a deterministic `insertId` (e.g., a hash of the record) to ensure idempotency at the storage layer.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
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: The pipeline does not set the insertId parameter in the BigQuery streaming output. — Option D is correct because BigQuery's streaming API uses the `insertId` parameter to deduplicate records within the streaming buffer. Without a unique `insertId`, BigQuery cannot detect and discard duplicate inserts that may occur when Dataflow retries a write after a worker restart. Even with exactly-once processing in the pipeline, the BigQuery streaming endpoint itself is at-least-once, so the `insertId` is essential for deduplication.
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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Last reviewed: Jun 30, 2026
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.
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