- A
Implement a retry mechanism in the Pub/Sub subscription to ensure no messages are lost.
Why wrong: Pub/Sub already guarantees at-least-once delivery; retries are unnecessary here.
- B
Enable Cloud Logging for all pipeline steps and analyze the logs for dropped elements.
Why wrong: Logging everything is expensive and generates too much noise; a targeted metric is more efficient.
- C
Add a global window with a late-data trigger to capture any data arriving after the window ends.
Why wrong: This could cause duplicate processing and does not address missing data within the window.
- D
Use Dataflow’s built-in metrics to compare the number of elements read from Pub/Sub and written to BigQuery for each window.
This identifies exactly where data is lost, enabling targeted debugging without overhead.
PDE Ensuring solution quality Practice Question
This PDE practice question tests your understanding of ensuring solution quality. 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 company runs a large Dataflow pipeline that aggregates user activity data from Pub/Sub into BigQuery every 10 minutes using fixed windows. Recently, the daily summary reports have shown 5-10% lower user engagement for certain segments compared to historical trends. The pipeline is completing successfully with no errors in Cloud Monitoring, and the Dataflow job dashboard shows all steps in green. There are no alarms. The team suspects data is being dropped or missed. They have verified that the Pub/Sub topic is receiving data correctly. After reviewing the pipeline code, they find that the pipeline uses a global window with a default 10-minute trigger, and writes results to a single BigQuery table partitioned by date. They also use exactly-once processing mode. Which of the following is the most likely cause and the best course of action to diagnose and fix the data quality issue?
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.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 Dataflow’s built-in metrics to compare the number of elements read from Pub/Sub and written to BigQuery for each window.
Option D is correct because the pipeline uses a global window with a default 10-minute trigger, which means data is processed in micro-batches but the global window never closes, so late-arriving data is included. However, the team suspects data is being dropped, and the most direct way to diagnose this is to compare the number of elements read from Pub/Sub (using the Pub/Sub subscription's 'pubsub_subscription' metric) with the number of elements written to BigQuery (using the BigQuery sink's 'bigquery_rows_written' metric) for each window. This comparison will reveal if any data is lost between reading and writing, which is a common issue when using exactly-once processing mode with streaming inserts that may silently fail due to schema mismatches or quota limits.
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.
- ✗
Implement a retry mechanism in the Pub/Sub subscription to ensure no messages are lost.
Why it's wrong here
Pub/Sub already guarantees at-least-once delivery; retries are unnecessary here.
- ✗
Enable Cloud Logging for all pipeline steps and analyze the logs for dropped elements.
Why it's wrong here
Logging everything is expensive and generates too much noise; a targeted metric is more efficient.
- ✗
Add a global window with a late-data trigger to capture any data arriving after the window ends.
Why it's wrong here
This could cause duplicate processing and does not address missing data within the window.
- ✓
Use Dataflow’s built-in metrics to compare the number of elements read from Pub/Sub and written to BigQuery for each window.
Why this is correct
This identifies exactly where data is lost, enabling targeted debugging without overhead.
Clue confirmation
The clue words "best", "most likely" 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 trap here is that candidates assume 'exactly-once processing' guarantees no data loss, but in reality, exactly-once only ensures no duplicates, not that all data is successfully written to the sink; silent failures in streaming inserts to BigQuery can cause data to be dropped without triggering pipeline errors.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow's exactly-once processing mode uses a combination of checkpointing and deduplication to ensure each element is processed once, but this does not guarantee that all elements are successfully written to BigQuery; for example, if a row violates a BigQuery schema constraint (e.g., a string that is too long), the streaming insert may fail silently and the element is dropped without an error in the pipeline. The built-in metrics 'pubsub_subscription/acknowledged_messages_count' and 'bigquery_rows_written' are exposed via Cloud Monitoring and can be queried per window using the Dataflow job's step-level metrics, allowing precise identification of data loss.
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: Use Dataflow’s built-in metrics to compare the number of elements read from Pub/Sub and written to BigQuery for each window. — Option D is correct because the pipeline uses a global window with a default 10-minute trigger, which means data is processed in micro-batches but the global window never closes, so late-arriving data is included. However, the team suspects data is being dropped, and the most direct way to diagnose this is to compare the number of elements read from Pub/Sub (using the Pub/Sub subscription's 'pubsub_subscription' metric) with the number of elements written to BigQuery (using the BigQuery sink's 'bigquery_rows_written' metric) for each window. This comparison will reveal if any data is lost between reading and writing, which is a common issue when using exactly-once processing mode with streaming inserts that may silently fail due to schema mismatches or quota limits.
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", "most likely". 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.
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Last reviewed: Jun 24, 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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