PDE Ingesting and Processing the Data Practice Question
You are designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. The pipeline must handle late-arriving data and emit correct results. You need to ensure that the pipeline's windowing and triggering strategy produces accurate aggregations. Which combination of windowing and triggering should you use?
⚠ Common exam trap
The trap here is assuming that the default trigger with zero allowed lateness is sufficient for late data, when in fact late elements are discarded after the watermark passes unless allowed lateness is set.
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
✓
Fixed windows with a trigger that fires after watermark passes and allowed lateness greater than 0.
To handle late-arriving data and produce accurate aggregations, you need a windowing strategy that allows late elements to be incorporated. Fixed windows with a trigger that fires after the watermark and a positive allowed lateness achieve this: the trigger emits results when the watermark passes, and any late data within the allowed lateness is added to the window, causing the trigger to fire again and update the results. This ensures completeness and correctness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Session windows with default trigger and no allowed lateness.
Why it's wrong here
Session windows group elements by activity gaps and are useful for user sessions, but they are not ideal for time-based aggregations that require handling late data. The default trigger with no allowed lateness would drop late data, leading to incomplete results. Session windows also have variable sizes, making it harder to produce periodic updates. This combination does not meet the requirement for accurate aggregations with late data.
- ✓
Fixed windows with a trigger that fires after watermark passes and allowed lateness greater than 0.
Why this is correct
Fixed windows with a trigger that fires after the watermark passes the window end, combined with allowed lateness greater than 0, ensures that late data within the allowed lateness period is included in the window's aggregation. This setup produces accurate results for both on-time and late-arriving data. The trigger can also be configured to fire early or on repeated updates, but the key is to allow lateness so that late elements are not dropped.
- ✗
Sliding windows with repeated trigger and allowed lateness set to a positive value.
Why it's wrong here
Sliding windows produce overlapping windows, which can complicate aggregation logic and increase cost. While a repeated trigger and positive allowed lateness can handle late data, sliding windows are not typically used for simple aggregations because each element belongs to multiple windows, potentially causing duplicate counting unless carefully managed. For accurate aggregations with late data, fixed or session windows with appropriate triggers are more common.
- ✗
Fixed windows with default trigger and allowed lateness of 0.
Why it's wrong here
Fixed windows with the default trigger emit results when the watermark passes the end of the window. With allowed lateness of 0, any data arriving after the watermark is dropped, leading to incomplete aggregations for late data. This is not suitable when late-arriving data must be handled correctly. The default trigger also does not provide early or speculative results, which may be needed for timely updates.
Go deeper
Related to this question
About these practice questions
One of 747 original PDE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.