The answer is to enable exactly-once sinks to BigQuery via Dataflow. This is correct because when a Dataflow job uses a 10-minute trigger on a global window, it can produce multiple output panes, each writing the same data to Cloud Storage, which then leads to duplicate rows when loaded into BigQuery. Exactly-once sinks solve this by leveraging idempotent writes and built-in deduplication, ensuring each record is written only once regardless of how many panes the trigger fires. On the Google Professional Data Engineer exam, this scenario tests your understanding of streaming data integrity and the trade-offs between windowing, triggers, and sink semantics—a common trap is to overcomplicate the fix by adjusting the trigger frequency or window size when the real issue is sink idempotency. Remember the memory tip: “Triggers cause panes, but exactly-once sinks break the chains.”
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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 data pipeline uses Cloud Pub/Sub to ingest events, then a Dataflow job writes to Cloud Storage in Avro format. The Dataflow job uses Global windows with a 10-minute trigger. The data is later loaded into BigQuery. They notice duplicate rows in BigQuery because the trigger produced multiple panes. What should the Dataflow pipeline change to eliminate duplicates?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable exactly-once sink to BigQuery via Dataflow
Option A is correct because enabling exactly-once sinks in Dataflow ensures that each record is written to the sink only once, even if the pipeline produces multiple panes due to triggers. In this scenario, the 10-minute trigger on a global window causes multiple output panes, leading to duplicate rows in BigQuery. Exactly-once sinks use idempotent writes and deduplication mechanisms to prevent duplicates, directly addressing the issue without changing the windowing or trigger logic.
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.
✓
Enable exactly-once sink to BigQuery via Dataflow
Why this is correct
Dataflow's exactly-once sink to BigQuery uses record IDs to deduplicate, preventing duplicates caused by trigger panes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a sharded output to Cloud Storage with unique filenames
Why it's wrong here
Sharded output can avoid overwrites but does not prevent multiple panes from producing duplicate rows that are later loaded.
✗
Write to a staging table and use a MERGE statement in BigQuery
Why it's wrong here
This is a post-processing step, not a pipeline change, and requires additional scheduling.
✗
Use a session window instead of global window
Why it's wrong here
Session windows group events by activity, but do not eliminate duplicates from multiple trigger panes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that changing windowing or output file naming can solve duplicate data issues, when the real solution is to enable exactly-once processing guarantees at the sink level.
Trap categories for this question
Command / output trap
Sharded output can avoid overwrites but does not prevent multiple panes from producing duplicate rows that are later loaded.
Detailed technical explanation
How to think about this question
Exactly-once sinks in Dataflow leverage the Beam sink API with idempotent writes and a deduplication cache (e.g., using a unique ID per record). Under the hood, Dataflow tracks each element's processing state to ensure that even if a pane is replayed, the sink ignores duplicates. In practice, this is critical for pipelines with frequent triggers or late-arriving data, where multiple panes can produce the same records, and without exactly-once semantics, downstream systems like BigQuery would see duplicates.
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 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.
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.
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: Enable exactly-once sink to BigQuery via Dataflow — Option A is correct because enabling exactly-once sinks in Dataflow ensures that each record is written to the sink only once, even if the pipeline produces multiple panes due to triggers. In this scenario, the 10-minute trigger on a global window causes multiple output panes, leading to duplicate rows in BigQuery. Exactly-once sinks use idempotent writes and deduplication mechanisms to prevent duplicates, directly addressing the issue without changing the windowing or trigger logic.
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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Question Discussion
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