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Question 906 of 1,000

Ensuring solution quality →hardMultiple ChoiceObjective-mapped

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: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

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.

Full breakdown with real-world context →

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 fixed 10-minute windows, and the team suspects data is being dropped. Using Dataflow's built-in metrics to compare the number of elements read from Pub/Sub with the number written to BigQuery for each window directly reveals any data loss. This comparison can identify issues such as schema mismatches or quota limits that cause silent failures in streaming inserts, which are not surfaced as pipeline errors.

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 word "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 is that candidates may assume exactly-once processing guarantees no data loss, but it only prevents duplicates. Silent failures in BigQuery streaming inserts can drop data without pipeline errors, and comparing input/output metrics is the best diagnostic approach.

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 fixed 10-minute windows, and the team suspects data is being dropped. Using Dataflow's built-in metrics to compare the number of elements read from Pub/Sub with the number written to BigQuery for each window directly reveals any data loss. This comparison can identify issues such as schema mismatches or quota limits that cause silent failures in streaming inserts, which are not surfaced as pipeline errors.

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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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