Quick Answer
The answer is feature distribution drift and prediction distribution drift. Feature distribution drift is correct because it directly measures changes in the input data over time using statistical tests like the Kolmogorov-Smirnov (KS) test, which compares the cumulative distribution of a feature in the current batch against a reference baseline—a primary indicator of data drift even when labels are unavailable. Prediction distribution drift is equally critical, as it captures shifts in the model’s output scores across batches, revealing when the model’s behavior changes due to upstream data shifts. On the Google Professional Machine Learning Engineer exam, this question tests your ability to distinguish between monitoring input features versus outputs; a common trap is choosing label drift, which requires ground truth labels often absent in batch prediction. Remember the mnemonic “Inputs and Outputs” to recall that both feature and prediction distributions must be tracked for robust data drift detection.
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.
Which TWO metrics should you monitor to detect data drift in a batch prediction pipeline?
Model accuracy on recent labeled data
Why wrong: Labels are delayed; not suitable for early drift detection.
Model prediction latency
Why wrong: Latency is a performance metric, not drift.
Feature distribution drift (e.g., KS test)
Directly measures input drift.
Prediction distribution drift
Can indicate drift even without labels.
Training data size
Why wrong: Unrelated to drift.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Feature distribution drift (e.g., KS test)
Feature distribution drift (C) is correct because it directly measures changes in the input data distribution over time using statistical tests like the Kolmogorov-Smirnov (KS) test, which compares the cumulative distribution of a feature in the current batch against a reference baseline. This is a primary indicator of data drift, as shifts in feature distributions can degrade model performance even if labels are not immediately available.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Model accuracy on recent labeled data
Why it's wrong here
Labels are delayed; not suitable for early drift detection.
Model prediction latency
Why it's wrong here
Latency is a performance metric, not drift.
Feature distribution drift (e.g., KS test)
Why this is correct
Directly measures input drift.
Related concept
Read the scenario before looking for a memorised answer.
Prediction distribution drift
Why this is correct
Can indicate drift even without labels.
Related concept
Read the scenario before looking for a memorised answer.
Training data size
Why it's wrong here
Unrelated to drift.
Common exam traps
Google Cloud often tests the distinction between monitoring for data drift (input distribution changes) versus monitoring for model performance degradation (accuracy), leading candidates to incorrectly select accuracy as a drift metric when it is actually a downstream effect.
Detailed technical explanation
The KS test quantifies the maximum distance between the empirical cumulative distribution functions of a feature in the reference and current windows; a p-value below a threshold (e.g., 0.05) indicates significant drift. In practice, monitoring prediction distribution drift (D) alongside feature drift provides a dual signal: prediction drift can occur even if individual features appear stable, due to changes in feature interactions or model calibration. Real-world pipelines often compute these metrics on sliding windows (e.g., 7-day vs. 30-day) to balance sensitivity and noise.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Feature distribution drift (e.g., KS test) — Feature distribution drift (C) is correct because it directly measures changes in the input data distribution over time using statistical tests like the Kolmogorov-Smirnov (KS) test, which compares the cumulative distribution of a feature in the current batch against a reference baseline. This is a primary indicator of data drift, as shifts in feature distributions can degrade model performance even if labels are not immediately available.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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