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 data pipeline using Cloud Pub/Sub and Cloud Dataflow is experiencing duplicate messages. The source system publishes messages at least once. What Dataflow technique ensures exactly-once processing?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Use idempotent sinks
Idempotent sinks allow safe duplicate writes, achieving exactly-once.
Use GlobalWindows
Why wrong: GlobalWindows are a windowing strategy, not a duplicate handling mechanism.
Set watermark threshold
Why wrong: Watermark affects late data handling, not duplicate elimination.
Enable streaming engine
Why wrong: Streaming engine improves performance but does not address duplicates.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use idempotent sinks
Option A is correct because idempotent sinks ensure that even if Cloud Pub/Sub delivers the same message multiple times (due to its at-least-once delivery semantics), the Dataflow pipeline can deduplicate or safely reapply the same data without causing duplicates in the output. This is achieved by designing the sink (e.g., BigQuery with insertId, Cloud Storage with unique filenames) to recognize and ignore repeated writes, effectively providing exactly-once processing semantics downstream.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use idempotent sinks
Why this is correct
Idempotent sinks allow safe duplicate writes, achieving exactly-once.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Use GlobalWindows
Why it's wrong here
GlobalWindows are a windowing strategy, not a duplicate handling mechanism.
Set watermark threshold
Why it's wrong here
Watermark affects late data handling, not duplicate elimination.
Enable streaming engine
Why it's wrong here
Streaming engine improves performance but does not address duplicates.
Common exam traps
The trap here is that candidates confuse 'exactly-once processing' with 'exactly-once delivery' from the source, but Pub/Sub only guarantees at-least-once delivery, so the responsibility for deduplication falls on the Dataflow pipeline and its sink design, not on windowing or engine settings.
Detailed technical explanation
Under the hood, Cloud Pub/Sub uses a unique ack ID for each message, but due to network retries or client-side timeouts, the same message may be redelivered. Dataflow's source-side deduplication relies on the Pub/Sub subscription's message ID, but for exactly-once processing to the sink, the pipeline must use idempotent writes—for example, BigQuery's streaming inserts with a user-provided insertId, which BigQuery uses to automatically deduplicate within a short time window (typically minutes). In real-world scenarios, a pipeline processing financial transactions must use idempotent sinks to avoid double-counting, even if Pub/Sub occasionally redelivers.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Use idempotent sinks — Option A is correct because idempotent sinks ensure that even if Cloud Pub/Sub delivers the same message multiple times (due to its at-least-once delivery semantics), the Dataflow pipeline can deduplicate or safely reapply the same data without causing duplicates in the output. This is achieved by designing the sink (e.g., BigQuery with insertId, Cloud Storage with unique filenames) to recognize and ignore repeated writes, effectively providing exactly-once processing semantics downstream.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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