- A
Use exactly-once processing by setting the pipeline's streaming engine to exactly-once and using a BigQuery sink with exactly-once semantics
Dataflow's exactly-once sink for BigQuery ensures no duplicates even with late data, using a combination of idempotent writes and deduplication.
- B
Use at-least-once processing and rely on BigQuery's automatic deduplication
Why wrong: BigQuery streaming inserts have a best-effort dedup window (typically 1 hour), but it is not guaranteed for late data beyond that.
- C
Use exactly-once processing and write results to a staging table, then use a scheduled merge query to combine with the main table
Why wrong: This adds complexity and latency; not the standard approach.
- D
Use at-most-once processing to guarantee no duplicates, and accept data loss
Why wrong: Data loss is not acceptable in this scenario.
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.
You are designing a Dataflow pipeline that reads from Pub/Sub, aggregates events into 10-minute windows, and writes the results to BigQuery. The pipeline must reliably handle late-arriving data (up to 1 hour) and prevent duplicate aggregations. Which combination of pipeline options should you use?
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
Use exactly-once processing by setting the pipeline's streaming engine to exactly-once and using a BigQuery sink with exactly-once semantics
To prevent duplicate aggregations, you need exactly-once processing. Dataflow supports exactly-once sinks (like BigQuery) when using the FILE_LOADS method or streaming inserts with exactly-once semantics. Using at-least-once with deduplication in BigQuery is not reliable. Exactly-once semantics require idempotent writes; the recommended approach is to use the BigQuery sink with exactly-once support (by setting the trigger frequency appropriately and using a dedup key). However, the simplest way is to use the Dataflow streaming engine and the BigQuery sink with exactly-once enabled. Among the options, using the BigQuery streaming inserts with exactly-once semantics (available in Dataflow) is correct.
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.
- ✓
Use exactly-once processing by setting the pipeline's streaming engine to exactly-once and using a BigQuery sink with exactly-once semantics
Why this is correct
Dataflow's exactly-once sink for BigQuery ensures no duplicates even with late data, using a combination of idempotent writes and deduplication.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use at-least-once processing and rely on BigQuery's automatic deduplication
Why it's wrong here
BigQuery streaming inserts have a best-effort dedup window (typically 1 hour), but it is not guaranteed for late data beyond that.
- ✗
Use exactly-once processing and write results to a staging table, then use a scheduled merge query to combine with the main table
Why it's wrong here
This adds complexity and latency; not the standard approach.
- ✗
Use at-most-once processing to guarantee no duplicates, and accept data loss
Why it's wrong here
Data loss is not acceptable in this scenario.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Scenario analysis trap
Data loss is not acceptable in this scenario.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PDE question test?
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: Use exactly-once processing by setting the pipeline's streaming engine to exactly-once and using a BigQuery sink with exactly-once semantics — To prevent duplicate aggregations, you need exactly-once processing. Dataflow supports exactly-once sinks (like BigQuery) when using the FILE_LOADS method or streaming inserts with exactly-once semantics. Using at-least-once with deduplication in BigQuery is not reliable. Exactly-once semantics require idempotent writes; the recommended approach is to use the BigQuery sink with exactly-once support (by setting the trigger frequency appropriately and using a dedup key). However, the simplest way is to use the Dataflow streaming engine and the BigQuery sink with exactly-once enabled. Among the options, using the BigQuery streaming inserts with exactly-once semantics (available in Dataflow) is correct.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 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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