The answer is the Kolmogorov-Smirnov (KS) statistic and Jensen-Shannon divergence (JSD). Both are correct because they are the two metrics natively supported by Vertex AI Model Monitoring for numerical data drift detection, each measuring distributional differences between training and serving data in complementary ways. The KS statistic quantifies the maximum distance between two empirical cumulative distribution functions, making it sensitive to shifts in central tendency and spread, while JSD provides a symmetric, bounded (0 to 1) measure of divergence between probability distributions, offering a smoothed and normalized alternative to Kullback-Leibler divergence. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI’s built-in monitoring capabilities versus custom approaches—a common trap is choosing metrics like KL divergence or population stability index, which are not natively supported for numerical features. Remember the mnemonic: “KS for cumulative shifts, JSD for symmetric divergence—both are Vertex AI’s drift gifts.”
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 has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Jensen-Shannon divergence (JSD)
Jensen-Shannon divergence (JSD) is a symmetric, bounded (0 to 1) measure of the difference between two probability distributions, making it ideal for detecting drift in numerical features by comparing the training distribution to the serving distribution. It is a smoothed and normalized version of Kullback-Leibler divergence, and Vertex AI Prediction's Model Monitoring natively supports JSD for numerical feature drift detection.
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.
✗
Pearson correlation coefficient
Why it's wrong here
Pearson correlation measures linear relationship between two variables, not distribution drift.
✓
Jensen-Shannon divergence (JSD)
Why this is correct
JSD measures similarity between two probability distributions and works for numerical features after binning.
Related concept
Read the scenario before looking for a memorised answer.
✗
Chi-squared statistic
Why it's wrong here
Chi-squared test is for categorical features.
✗
Population Stability Index (PSI)
Why it's wrong here
PSI is primarily used for categorical features or binned numerical data, but not as a direct metric for continuous distributions.
✓
Kolmogorov-Smirnov (KS) statistic
Why this is correct
KS test is used to compare two distributions and detect shift in numerical features.
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 misconception that Pearson correlation or Chi-squared are appropriate for numerical drift, when in fact Pearson measures correlation between two variables and Chi-squared is for categorical data, leading candidates to overlook the correct distribution-comparison metrics like JSD and KS.
Detailed technical explanation
How to think about this question
JSD is computed as the square root of the Jensen-Shannon divergence, which averages the Kullback-Leibler divergences of each distribution to their mixture distribution, ensuring symmetry and avoiding infinite values when distributions have zero-probability bins. The Kolmogorov-Smirnov (KS) statistic measures the maximum absolute difference between the empirical cumulative distribution functions (ECDFs) of two samples, making it sensitive to any type of distributional shift (location, scale, shape) and is commonly used in Vertex AI's drift detection for numerical features. In practice, combining JSD and KS provides complementary views: JSD captures overall distributional overlap, while KS pinpoints specific threshold shifts.
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.
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: Jensen-Shannon divergence (JSD) — Jensen-Shannon divergence (JSD) is a symmetric, bounded (0 to 1) measure of the difference between two probability distributions, making it ideal for detecting drift in numerical features by comparing the training distribution to the serving distribution. It is a smoothed and normalized version of Kullback-Leibler divergence, and Vertex AI Prediction's Model Monitoring natively supports JSD for numerical feature drift detection.
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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Question Discussion
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