- A
Cloud Functions for post-processing
Why wrong: Cloud Functions are stateless and not designed for maintaining deduplication state across a pipeline.
- B
BigQuery streaming inserts with a unique key for deduplication
Why wrong: BigQuery streaming inserts do not natively deduplicate; users must implement dedup logic externally.
- C
Cloud Spanner for deduplication state across the pipeline
Using Cloud Spanner as a global state store allows tracking processed event IDs for deduplication.
- D
Cloud Pub/Sub with duplicate detection (using message IDs)
Pub/Sub can detect and prevent duplicate publications, reducing duplicates at the source.
- E
Dataflow with idempotent write operations to BigQuery
Dataflow can implement idempotent BigQuery sinks using unique row identifiers to avoid duplicates.
Quick Answer
The answer is Dataflow with idempotent write operations to BigQuery, Cloud Spanner for deduplication state, and Pub/Sub with exactly-once delivery. These three services work together to guarantee exactly-once processing in streaming pipelines by addressing different failure points: Dataflow’s checkpointing combined with idempotent sinks prevents duplicate writes, Cloud Spanner provides globally consistent transactions to track processed event IDs, and Pub/Sub’s exactly-once delivery ensures each message is received only once from the source. On the Google Professional Data Engineer exam, this question tests your understanding of how to enforce end-to-end exactly-once semantics rather than just at-least-once delivery. A common trap is choosing BigQuery streaming inserts alone, which do not guarantee deduplication without idempotent writes. Remember the mnemonic “SID” — Spanner for state, Idempotent sinks, and Delivery from Pub/Sub — to recall the three pillars of exactly-once processing.
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 streaming pipeline that must guarantee exactly-once processing. Which three services or features can help achieve this? (Choose THREE.)
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
Cloud Spanner for deduplication state across the pipeline
Cloud Spanner is correct because it provides globally distributed, strongly consistent transactions that can be used to maintain deduplication state across the entire streaming pipeline. By storing a unique key for each processed event in Spanner, the pipeline can atomically check and record whether an event has already been handled, ensuring exactly-once semantics even in the face of retries or failures.
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.
- ✗
Cloud Functions for post-processing
Why it's wrong here
Cloud Functions are stateless and not designed for maintaining deduplication state across a pipeline.
- ✗
BigQuery streaming inserts with a unique key for deduplication
Why it's wrong here
BigQuery streaming inserts do not natively deduplicate; users must implement dedup logic externally.
- ✓
Cloud Spanner for deduplication state across the pipeline
Why this is correct
Using Cloud Spanner as a global state store allows tracking processed event IDs for deduplication.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Cloud Pub/Sub with duplicate detection (using message IDs)
Why this is correct
Pub/Sub can detect and prevent duplicate publications, reducing duplicates at the source.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Dataflow with idempotent write operations to BigQuery
Why this is correct
Dataflow can implement idempotent BigQuery sinks using unique row identifiers to avoid duplicates.
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 BigQuery streaming inserts can guarantee exactly-once processing via a unique key, when in fact BigQuery only supports at-least-once delivery and requires external deduplication mechanisms like Cloud Spanner or Dataflow with idempotent writes.
Detailed technical explanation
How to think about this question
Cloud Spanner uses TrueTime and Paxos-based replication to provide external consistency, making it ideal for deduplication state that must be accurate across regions. In a streaming pipeline, you can use a Spanner table with a primary key derived from the event ID and perform a read-modify-write or insert-if-not-exists operation to ensure each event is processed only once. This approach is critical in financial or IoT scenarios where duplicate events could cause double billing or incorrect sensor readings.
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?
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: Cloud Spanner for deduplication state across the pipeline — Cloud Spanner is correct because it provides globally distributed, strongly consistent transactions that can be used to maintain deduplication state across the entire streaming pipeline. By storing a unique key for each processed event in Spanner, the pipeline can atomically check and record whether an event has already been handled, ensuring exactly-once semantics even in the face of retries or failures.
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.
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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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