PMLE Monitoring ML Solutions Practice Question
A team has deployed a model on a Vertex AI Endpoint and enabled Vertex AI Model Monitoring for feature skew. They notice that the skew metric for a categorical feature with high cardinality is consistently high, even though the feature's distribution appears stable to the team. What is the most likely cause of this high skew metric?
⚠ Common exam trap
The trap here is assuming that high skew always indicates a shift in the overall distribution, when it can be caused by new or missing categories in high-cardinality features.
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
✓
The training dataset and live data have different sets of categories for that feature, causing divergence.
For high-cardinality categorical features, the presence of categories in live data that were not in the training data (or vice versa) can cause large divergence in distribution comparisons. Vertex AI Model Monitoring uses metrics like Jensen-Shannon divergence, which are sensitive to disjoint category sets. This mismatch can result in consistently high skew even if the overall distribution appears stable. The correct cause is the difference in category sets between training and live data.
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 training dataset and live data have different sets of categories for that feature, causing divergence.
Why this is correct
High-cardinality categorical features often have categories present in live data that were not in the training data, or vice versa. This mismatch in category sets leads to large divergence metrics, such as Jensen-Shannon, because the distributions have disjoint support. Even if the overall distribution seems stable, the presence of unseen categories can cause high skew. Therefore, this is the most likely cause.
- ✗
The monitoring job is sampling too few requests, leading to statistical noise.
Why it's wrong here
Low sampling can increase variance in the estimated distribution, potentially causing fluctuations. However, consistent high skew for a high-cardinality categorical feature is more likely due to the inherent difficulty of comparing many categories. Statistical noise would not consistently produce high skew if the underlying distribution is stable; it would cause random variation. Thus, sampling is not the primary cause.
- ✗
The model's predictions are drifting, which indirectly increases feature skew.
Why it's wrong here
Feature skew and prediction drift are independent metrics. Feature skew compares input distributions to training data; prediction drift compares output distributions over time. Prediction drift does not influence the computation of feature skew. Therefore, even if predictions are drifting, it would not cause the feature skew metric to be high. This option confuses the two monitoring types.
- ✗
The feature is not included in the training dataset schema, so the skew computation fails.
Why it's wrong here
If the feature were missing from the training dataset, the monitoring job would likely error or exclude it, not compute a high skew value. The scenario states the skew metric is high, implying the feature is monitored. Missing schema typically results in configuration errors, not inflated skew metrics. Thus, this is not the cause.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE 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 PMLE exam.