- A
Upload the ground truth labels to a BigQuery table with a schema that includes prediction timestamp and model version.
Correct approach; BigQuery is required for ground truth storage.
- B
Use Vertex AI Experiments to log ground truth alongside training runs.
Why wrong: Experiments are for tracking training runs, not production monitoring.
- C
Store the ground truth labels in Cloud Storage as CSV files and reference them in the monitoring config.
Why wrong: Ground truth must be in BigQuery, not Cloud Storage.
- D
Insert ground truth labels directly into the Vertex AI Endpoint's log sink.
Why wrong: Log sinks capture request/response logs, not ground truth.
Question 864 of 1,000
PMLE Monitoring ML Solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 team wants to collect ground truth labels for their model deployed on Vertex AI Endpoint to perform model quality monitoring. They have a process that generates actual outcomes within 24 hours of prediction. What is the recommended approach for storing these labels?
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
✓
Upload the ground truth labels to a BigQuery table with a schema that includes prediction timestamp and model version.
Vertex AI Model Monitoring for model quality requires ground truth data to be uploaded to BigQuery. Labels are stored in a BigQuery table with the prediction timestamp and model ID for comparison.
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.
- ✓
Upload the ground truth labels to a BigQuery table with a schema that includes prediction timestamp and model version.
Why this is correct
Correct approach; BigQuery is required for ground truth storage.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI Experiments to log ground truth alongside training runs.
Why it's wrong here
Experiments are for tracking training runs, not production monitoring.
- ✗
Store the ground truth labels in Cloud Storage as CSV files and reference them in the monitoring config.
Why it's wrong here
Ground truth must be in BigQuery, not Cloud Storage.
- ✗
Insert ground truth labels directly into the Vertex AI Endpoint's log sink.
Why it's wrong here
Log sinks capture request/response logs, not ground truth.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
Monitoring ML Solutions — This question tests Monitoring ML Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Upload the ground truth labels to a BigQuery table with a schema that includes prediction timestamp and model version. — Vertex AI Model Monitoring for model quality requires ground truth data to be uploaded to BigQuery. Labels are stored in a BigQuery table with the prediction timestamp and model ID for comparison.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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