- A
Feature value distribution skew (distance metrics).
Can detect shifts due to missing values being treated differently.
- B
Training-serving skew detection for all features.
Why wrong: Broader than just missing values.
- C
Total count of missing values across all features.
Why wrong: Less actionable than ratio per feature.
- D
Prediction confidence score.
Why wrong: Not related to missing values.
- E
Missing value ratio per feature.
Directly tracks proportion of missing values.
Question 949 of 1,000
mediumMultiple SelectObjective-mapped
PMLE Practice Question: A data science team uses Vertex AI Model…
This PMLE practice question tests your understanding of a data science team uses vertex ai model…. 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 data science team uses Vertex AI Model Monitoring to detect data quality issues in a production model. Which TWO metrics should they enable to identify problems with missing values in predictions? (Select TWO.)
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
✓
Feature value distribution skew (distance metrics).
Option A is correct because Vertex AI Model Monitoring's feature value distribution skew detection uses distance metrics (e.g., Jenson-Shannon divergence, L-infinity) to compare the distribution of feature values in the serving data against the training data. A sudden increase in missing values in a feature will shift its distribution, triggering a skew alert. This allows the team to detect missing value problems indirectly by monitoring distributional drift.
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.
- ✓
Feature value distribution skew (distance metrics).
Why this is correct
Can detect shifts due to missing values being treated differently.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Training-serving skew detection for all features.
Why it's wrong here
Broader than just missing values.
- ✗
Total count of missing values across all features.
Why it's wrong here
Less actionable than ratio per feature.
- ✗
Prediction confidence score.
Why it's wrong here
Not related to missing values.
- ✓
Missing value ratio per feature.
Why this is correct
Directly tracks proportion of missing values.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between aggregate metrics (like total count) and per-feature metrics (like ratio), and candidates mistakenly select 'total count of missing values across all features' because they think it directly addresses missing values, but Vertex AI Model Monitoring only supports per-feature missing value ratios.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Model Monitoring computes per-feature missing value ratios by comparing the number of null or NaN values in the serving request against the total number of requests in a sliding window. This ratio is then compared against a user-defined threshold (e.g., 0.05) to trigger an alert. In a real-world scenario, a sudden spike in missing values for a critical feature like 'age' could silently degrade model accuracy, but the missing value ratio metric catches this before it impacts business decisions.
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 PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Feature value distribution skew (distance metrics). — Option A is correct because Vertex AI Model Monitoring's feature value distribution skew detection uses distance metrics (e.g., Jenson-Shannon divergence, L-infinity) to compare the distribution of feature values in the serving data against the training data. A sudden increase in missing values in a feature will shift its distribution, triggering a skew alert. This allows the team to detect missing value problems indirectly by monitoring distributional drift.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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