The answer is that the 'income' feature is not present in the serving data. Vertex AI Model Monitoring’s skew detection works by comparing the distribution of a feature in the training data against its distribution in the live serving data; if the feature is entirely absent from the serving requests, the system has no data to compute a comparison, so it cannot generate a skew alert regardless of how much drift exists in other features. This scenario tests your understanding that skew detection is feature-specific and requires the monitored feature to actually appear in the inference calls. On the Google Professional Machine Learning Engineer exam, this is a common trap where candidates assume a low threshold or misconfigured monitoring is the issue, when the real problem is a missing column in the serving schema. A helpful memory tip: no feature, no skew—if the model never sees the feature at inference time, the monitor has nothing to compare.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. 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.
Refer to the exhibit. A team configured Vertex AI Model Monitoring with skew detection for feature "income" with a threshold of 0.2. However, they have not received any alerts even though they suspect data drift. What is the most likely reason?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The 'income' feature is not present in the serving data
If the 'income' feature is not present in the serving data, the skew detection cannot compute a comparison, and no alert is generated even if other drifts exist. The threshold being low would increase alerts, not suppress them. The monitoring likely is enabled since the config is present. The drift threshold for drift detection is separate.
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.
✗
The monitoring is not enabled for the endpoint
Why it's wrong here
The presence of a model monitoring config suggests it is enabled; otherwise the config would not be applied.
✓
The 'income' feature is not present in the serving data
Why this is correct
If the feature is missing from serving data, skew detection cannot perform comparison and will not generate alerts.
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.
✗
The actual skew is below the threshold
Why it's wrong here
If skew is below 0.2, no alert would be triggered, but the team suspects drift, making this unlikely as the cause of zero alerts.
✗
The drift detection threshold is set higher
Why it's wrong here
Drift detection is a separate configuration from skew detection; it does not affect skew alerts.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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.
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: The 'income' feature is not present in the serving data — If the 'income' feature is not present in the serving data, the skew detection cannot compute a comparison, and no alert is generated even if other drifts exist. The threshold being low would increase alerts, not suppress them. The monitoring likely is enabled since the config is present. The drift threshold for drift detection is separate.
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.
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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Question Discussion
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