- A
Use side inputs to join late data with the main stream.
Why wrong: Side inputs are not designed for late event handling; they are for supplementary data.
- B
Use streaming inserts into BigQuery and ignore late data.
Why wrong: Ignoring late data compromises data quality; proper handling is expected for reliable pipelines.
- C
Configure triggers with allowed lateness and accumulation of late firings.
This is the standard pattern for handling late data in Dataflow: set allowed lateness and use triggers to emit on late arrival.
- D
Increase the window duration to cover late data.
Why wrong: Larger windows increase latency and do not guarantee all late data is captured; they also change aggregation semantics.
Question 176 of 1,000
PDE Ensuring solution quality Practice Question
This PDE practice question tests your understanding of ensuring solution quality. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 Dataflow processes streaming data. Late-arriving events are currently being dropped. How should the team modify the pipeline to ensure late data is processed correctly?
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
✓
Configure triggers with allowed lateness and accumulation of late firings.
Option C is correct because Dataflow's triggers allow you to configure allowed lateness and accumulation of late firings, which ensures that late-arriving events are still processed within the correct window. By setting `withAllowedLateness` and specifying accumulation mode (e.g., `ACCUMULATING`), the pipeline will emit additional panes for late data and merge them with the existing window results, preventing data loss.
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 side inputs to join late data with the main stream.
Why it's wrong here
Side inputs are not designed for late event handling; they are for supplementary data.
- ✗
Use streaming inserts into BigQuery and ignore late data.
Why it's wrong here
Ignoring late data compromises data quality; proper handling is expected for reliable pipelines.
- ✓
Configure triggers with allowed lateness and accumulation of late firings.
Why this is correct
This is the standard pattern for handling late data in Dataflow: set allowed lateness and use triggers to emit on late arrival.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the window duration to cover late data.
Why it's wrong here
Larger windows increase latency and do not guarantee all late data is captured; they also change aggregation semantics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that simply increasing the window duration or using side inputs can handle late data in Dataflow, when in fact only trigger-based mechanisms with allowed lateness and accumulation provide the precise control needed for streaming late-arriving events.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow's `withAllowedLateness` sets a time duration after the watermark passes the end of a window during which late data is still accepted. When combined with accumulation triggers (e.g., `AfterWatermark.pastEndOfWindow().withLateFirings()`), the pipeline emits speculative and late panes, and the final result includes all data within the allowed lateness period. A subtle behavior is that if accumulation is set to `DISCARDING`, late firings will overwrite previous panes, which can lead to data loss if not configured correctly for the use case.
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?
Ensuring solution quality — This question tests Ensuring solution quality — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure triggers with allowed lateness and accumulation of late firings. — Option C is correct because Dataflow's triggers allow you to configure allowed lateness and accumulation of late firings, which ensures that late-arriving events are still processed within the correct window. By setting `withAllowedLateness` and specifying accumulation mode (e.g., `ACCUMULATING`), the pipeline will emit additional panes for late data and merge them with the existing window results, preventing data loss.
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: Jul 4, 2026
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