- A
Use Cloud Pub/Sub with synchronous pull and manually commit offsets after successfully writing to Bigtable.
Why wrong: Manual offset tracking is possible but not native; Dataflow handles this automatically with exactly-once.
- B
Use Dataflow with exactly-once processing, and ensure the Bigtable sink uses idempotent mutations based on the transaction ID.
Dataflow deduplicates records using unique identifiers; Bigtable idempotent writes (e.g., using CheckAndMutate) ensure that even if a mutation is retried, the result is the same.
- C
Use Dataflow with at-least-once processing and implement deduplication in a windowed transform using the transaction ID.
Why wrong: At-least-once processing in Dataflow can still cause duplicates if not combined with exactly-once checkpointing.
- D
Use Cloud Pub/Sub with exactly-once delivery enabled, and write to Bigtable using single-row transactions.
Why wrong: Cloud Pub/Sub does not support exactly-once delivery; it is at-least-once. Single-row transactions are insufficient for deduplication across multiple rows.
Quick Answer
The answer is to use Dataflow with exactly-once processing and idempotent Bigtable mutations keyed by the transaction ID. This combination is correct because Dataflow’s built-in checkpointing and source-side deduplication ensure each record from Pub/Sub is processed exactly once, while Bigtable’s idempotent writes—where the same transaction ID always produces the same result—prevent duplicates even if a mutation is retried. On the Google Professional Data Engineer exam, this scenario tests your understanding of how to bridge streaming guarantees with a NoSQL sink; a common trap is assuming Pub/Sub’s at-least-once delivery alone suffices, forgetting that the sink must also be idempotent. The key insight is that exactly-once is a pipeline property, not a single service feature. Memory tip: think “Dataflow dedup + Bigtable idempotent = end-to-end exactly-once.”
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.
You are designing a streaming data pipeline that must guarantee exactly-once processing semantics for financial transactions. The pipeline reads from Cloud Pub/Sub and writes to Cloud Bigtable. Each transaction has a unique transaction ID. Which features do you need to implement to ensure exactly-once semantics end-to-end?
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 Dataflow with exactly-once processing, and ensure the Bigtable sink uses idempotent mutations based on the transaction ID.
Option B is correct because Dataflow's exactly-once processing guarantees that each record is processed precisely once, and idempotent Bigtable mutations (keyed by transaction ID) ensure that even if a mutation is retried, the result is the same. This combination provides end-to-end exactly-once semantics: Dataflow handles source-side deduplication and checkpointing, while Bigtable's idempotent writes prevent duplicates at the sink.
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 Cloud Pub/Sub with synchronous pull and manually commit offsets after successfully writing to Bigtable.
Why it's wrong here
Manual offset tracking is possible but not native; Dataflow handles this automatically with exactly-once.
- ✓
Use Dataflow with exactly-once processing, and ensure the Bigtable sink uses idempotent mutations based on the transaction ID.
Why this is correct
Dataflow deduplicates records using unique identifiers; Bigtable idempotent writes (e.g., using CheckAndMutate) ensure that even if a mutation is retried, the result is the same.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Dataflow with at-least-once processing and implement deduplication in a windowed transform using the transaction ID.
Why it's wrong here
At-least-once processing in Dataflow can still cause duplicates if not combined with exactly-once checkpointing.
- ✗
Use Cloud Pub/Sub with exactly-once delivery enabled, and write to Bigtable using single-row transactions.
Why it's wrong here
Cloud Pub/Sub does not support exactly-once delivery; it is at-least-once. Single-row transactions are insufficient for deduplication across multiple rows.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that Pub/Sub's 'exactly-once delivery' feature exists or that manual offset management alone can achieve end-to-end exactly-once semantics, when in reality Pub/Sub only offers at-least-once delivery and requires a processing framework like Dataflow to achieve exactly-once end-to-end.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow's exactly-once processing relies on checkpointing and source-side deduplication via the Pub/Sub subscription's snapshot mechanism, which tracks acknowledged messages. Bigtable's idempotent mutations use the transaction ID as a row key or column qualifier, so repeated writes with the same ID overwrite the same cell, ensuring idempotency. In a real-world scenario, if a worker fails after writing to Bigtable but before committing the checkpoint, Dataflow will replay the record, and the idempotent mutation will produce the same state, preserving exactly-once semantics.
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
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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: Use Dataflow with exactly-once processing, and ensure the Bigtable sink uses idempotent mutations based on the transaction ID. — Option B is correct because Dataflow's exactly-once processing guarantees that each record is processed precisely once, and idempotent Bigtable mutations (keyed by transaction ID) ensure that even if a mutation is retried, the result is the same. This combination provides end-to-end exactly-once semantics: Dataflow handles source-side deduplication and checkpointing, while Bigtable's idempotent writes prevent duplicates at the sink.
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.
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 →
Same concept, more angles
1 more ways this is tested on PDE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is ingesting real-time sensor data from thousands of devices into Cloud Pub/Sub. They need to process this data with low latency (seconds) and exactly-once semantics. Which data processing service should they use?
easy- A.Cloud Run with Pub/Sub push
- B.Cloud Functions triggered by Pub/Sub
- ✓ C.Dataflow streaming with exactly-once processing
- D.Dataproc with Spark Streaming
Why C: Dataflow streaming with exactly-once processing is the correct choice because it provides exactly-once semantics for Pub/Sub sources via checkpointing and idempotent sinks, and it meets the low-latency (seconds) requirement through its streaming engine that minimizes per-element overhead. Cloud Dataflow's integration with Pub/Sub ensures that each message is processed exactly once, even in the presence of failures, by using snapshots and consistent state management.
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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