- A
Use a global window with a very long allowed lateness (e.g., 7 days).
Why wrong: Global windows retain state for all late data, leading to high state storage costs.
- B
Use session windows with a gap duration of 1 hour and allowed lateness of 2 days.
Why wrong: Session windows can merge unrelated events if gap is too large, and still incur high state cost.
- C
Use sliding windows with a short allowed lateness (e.g., 10 minutes) and a side input containing historical data.
Why wrong: Sliding windows with short lateness may drop late data, and side inputs add complexity.
- D
Use fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark.
Fixed windows with a realistic allowed lateness capture late data without excessive state cost, and a trivial watermark ensures no data is dropped.
Control Cost and Completeness When Handling Late Data in Dataflow
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 financial services company uses Dataflow pipelines with late data handling. They need to ensure that all late-arriving data is processed correctly but also want to control costs. What is the best configuration?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Quick Answer
The answer is to use fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark. This configuration directly addresses the core challenge of balancing completeness and cost in Dataflow late data handling: the fixed window bounds the state size, the generous allowed lateness ensures no late records are dropped, and the trivial watermark prevents the pipeline from advancing the watermark prematurely, which would otherwise discard late data. On the Google Professional Data Engineer exam, this scenario tests your understanding of how windowing strategies interact with watermark progression and state management—a common trap is choosing a global window, which can balloon state costs because it never triggers output until the stream ends, or session windows, which can incorrectly merge late events across unrelated sessions. The key memory tip is “fixed and forgiving”: fixed windows for cost control, generous allowed lateness for completeness, and a trivial watermark to keep the window open for the full delay period.
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 fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark.
Option D is correct because fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark ensure that all late-arriving data is processed while controlling costs. The trivial watermark (e.g., 0 or infinite) prevents the pipeline from dropping any late data, and the allowed lateness bounds the window's lifetime, avoiding excessive state storage. This balances completeness with cost by not keeping windows open indefinitely.
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 a global window with a very long allowed lateness (e.g., 7 days).
Why it's wrong here
Global windows retain state for all late data, leading to high state storage costs.
- ✗
Use session windows with a gap duration of 1 hour and allowed lateness of 2 days.
Why it's wrong here
Session windows can merge unrelated events if gap is too large, and still incur high state cost.
- ✗
Use sliding windows with a short allowed lateness (e.g., 10 minutes) and a side input containing historical data.
Why it's wrong here
Sliding windows with short lateness may drop late data, and side inputs add complexity.
- ✓
Use fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark.
Why this is correct
Fixed windows with a realistic allowed lateness capture late data without excessive state cost, and a trivial watermark ensures no data is dropped.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google Professional Data Engineer exam often tests the misconception that longer allowed lateness or global windows are always better for completeness, but the trap here is that they ignore cost implications and the need for bounded state; candidates may choose Option A or B without realizing the state explosion and lack of per-window output.
Detailed technical explanation
How to think about this question
In Dataflow (Apache Beam), allowed lateness controls how long the pipeline waits for late data before dropping it, while the watermark tracks event time progress. A trivial watermark (e.g., set to the current time or using a very slow watermark) effectively disables watermark-based dropping, allowing all late data to be processed until the allowed lateness expires. Fixed windows with a bounded allowed lateness (e.g., 2 days) limit the window's state retention, reducing storage costs compared to global windows, and ensure that each window emits a final result after the lateness period, which is critical for cost control in streaming pipelines.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
- →
Ensuring solution quality — study guide chapter
Learn the concepts, then practise the questions
- →
Ensuring solution quality practice questions
Targeted practice on this topic area only
- →
All PDE questions
1,000 questions across all exam domains
- →
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
- →
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Designing Data Processing Systems practice questions
Practise PDE questions linked to Designing Data Processing Systems.
Ingesting and Processing the Data practice questions
Practise PDE questions linked to Ingesting and Processing the Data.
Storing the Data practice questions
Practise PDE questions linked to Storing the Data.
Preparing and Using Data for Analysis practice questions
Practise PDE questions linked to Preparing and Using Data for Analysis.
Maintaining and Automating Data Workloads practice questions
Practise PDE questions linked to Maintaining and Automating Data Workloads.
Building and operationalizing data processing systems practice questions
Practise PDE questions linked to Building and operationalizing data processing systems.
Operationalizing machine learning models practice questions
Practise PDE questions linked to Operationalizing machine learning models.
Ensuring solution quality practice questions
Practise PDE questions linked to Ensuring solution quality.
PDE fundamentals practice questions
Practise PDE questions linked to PDE fundamentals.
PDE scenario practice questions
Practise PDE questions linked to PDE scenario.
PDE troubleshooting practice questions
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Start a free PDE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark. — Option D is correct because fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark ensure that all late-arriving data is processed while controlling costs. The trivial watermark (e.g., 0 or infinite) prevents the pipeline from dropping any late data, and the allowed lateness bounds the window's lifetime, avoiding excessive state storage. This balances completeness with cost by not keeping windows open indefinitely.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 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?
easy- A.Use side inputs to join late data with the main stream.
- B.Use streaming inserts into BigQuery and ignore late data.
- ✓ C.Configure triggers with allowed lateness and accumulation of late firings.
- D.Increase the window duration to cover late data.
Why C: 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.
Keep practising
More PDE practice questions
- A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated…
- A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a s…
- A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data qua…
- Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from B…
- A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into Big…
- A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a Tens…
Last reviewed: Jul 4, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.