Quick Answer
The answer is exactly-once processing, also known as exactly-once semantics (EOS) in Dataflow. This feature is correct because it combines source-side deduplication using Pub/Sub message IDs with a sink-side commit protocol to ensure that even if the pipeline restarts or fails, each record is processed exactly once, eliminating duplicates in the sink while still honoring at-least-once delivery from Pub/Sub. On the Google Professional Cloud Developer exam, this scenario tests your understanding of how Dataflow bridges the gap between Pub/Sub’s at-least-once guarantee and the need for a duplicate-free output; a common trap is confusing idempotent sinks with exactly-once processing, but the key is that EOS handles both deduplication and failure recovery. Remember the mnemonic: “EOS kills duplicates, not delivery.”
This PCD practice question tests your understanding of designing highly scalable, available, and reliable cloud-native applications. 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.
A developer is designing a data pipeline using Pub/Sub and Dataflow. They need to guarantee at-least-once delivery with no duplicates in the sink. Which Dataflow feature should they use?
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.
Exactly-once processing
Exactly-once processing ensures each record is processed once, eliminating duplicates in the sink.
Checkpointing
Why wrong: Checkpointing saves progress but does not prevent duplicates.
Idempotent writes
Why wrong: Idempotent writes help avoid duplicates if retried, but do not guarantee exactly-once processing.
Windowing
Why wrong: Windowing groups events by time, but does not prevent 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
Exactly-once processing
Option A is correct because Dataflow's exactly-once processing (also known as 'exactly-once semantics' or 'EOS') ensures that each record is processed exactly once, even if the pipeline restarts or fails. This eliminates duplicates in the sink while still guaranteeing at-least-once delivery from Pub/Sub, because Dataflow uses a combination of source-side deduplication (via Pub/Sub message IDs) and sink-side idempotent writes (via the Dataflow sink's commit protocol). The result is that no duplicate records are written to the sink, meeting the requirement of no duplicates.
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.
Exactly-once processing
Why this is correct
Exactly-once processing ensures each record is processed once, eliminating duplicates in the sink.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Checkpointing
Why it's wrong here
Checkpointing saves progress but does not prevent duplicates.
Idempotent writes
Why it's wrong here
Idempotent writes help avoid duplicates if retried, but do not guarantee exactly-once processing.
Windowing
Why it's wrong here
Windowing groups events by time, but does not prevent duplicates.
Common exam traps
The trap here is that candidates confuse 'idempotent writes' (a sink-side property) with Dataflow's built-in 'exactly-once processing' feature, or they mistakenly think checkpointing alone eliminates duplicates, when in fact checkpointing only saves state and does not prevent duplicate writes to the sink.
Detailed technical explanation
Under the hood, Dataflow's exactly-once processing relies on a two-phase commit protocol between the pipeline workers and the sink, where the sink must support atomic commits (e.g., BigQuery's streaming inserts with a deduplication key, or Cloud Storage's rename-on-commit). For Pub/Sub sources, Dataflow uses the Pub/Sub subscription's message acknowledgment ID to track which messages have been processed, and it only commits the output after the source messages are acknowledged, ensuring that a message is not replayed unless the entire batch fails. A real-world scenario where this matters is a financial transaction pipeline: if a payment event is processed twice due to a worker crash, exactly-once processing prevents double-charging the customer, whereas checkpointing alone would not guarantee this.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Designing highly scalable, available, and reliable cloud-native applications — This question tests Designing highly scalable, available, and reliable cloud-native applications — Read the scenario before looking for a memorised answer..
The correct answer is: Exactly-once processing — Option A is correct because Dataflow's exactly-once processing (also known as 'exactly-once semantics' or 'EOS') ensures that each record is processed exactly once, even if the pipeline restarts or fails. This eliminates duplicates in the sink while still guaranteeing at-least-once delivery from Pub/Sub, because Dataflow uses a combination of source-side deduplication (via Pub/Sub message IDs) and sink-side idempotent writes (via the Dataflow sink's commit protocol). The result is that no duplicate records are written to the sink, meeting the requirement of no duplicates.
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 25, 2026
This PCD 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 PCD exam.
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