This PMLE practice question tests your understanding of monitoring ml solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company uses Vertex AI Model Monitoring to detect data drift. They have a model that predicts house prices. Which dataset should they compare against the training data to detect drift?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The latest batch of predictions
Option C is correct because Vertex AI Model Monitoring compares the training data (serving as the baseline) against the latest batch of predictions to detect data drift. This batch represents the most recent inference requests, allowing the monitoring service to compute statistical distribution differences (e.g., Jensen-Shannon divergence) and trigger alerts when drift exceeds a configured threshold. Using the latest batch ensures timely detection of shifts in the production data distribution.
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 entire historical prediction data
Why it's wrong here
Historical data may include outdated patterns and dilute recent drift.
✗
A random sample of recent predictions
Why it's wrong here
Model Monitoring typically uses the full latest batch of predictions, not a sample.
✓
The latest batch of predictions
Why this is correct
Comparing the latest serving data distribution to training data detects drift.
Related concept
Read the scenario before looking for a memorised answer.
✗
The validation data used during training
Why it's wrong here
Validation data is used for model evaluation, not serving drift detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between 'recent predictions' and 'latest batch' — the trap is that candidates confuse a random sample (which is statistically valid for inference but not for drift detection) with the complete batch that Vertex AI requires for accurate distribution comparison.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Model Monitoring uses statistical tests like the Kolmogorov-Smirnov test for numerical features and the chi-squared test for categorical features, comparing the training data distribution against the latest prediction batch distribution. The service also supports drift thresholds per feature and can automatically retrain models via Vertex AI Pipelines when drift is detected. In real-world scenarios, a model predicting house prices might see drift if the local economy shifts (e.g., a new tech hub raises median income), and only the latest batch of predictions captures that sudden change.
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
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 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: The latest batch of predictions — Option C is correct because Vertex AI Model Monitoring compares the training data (serving as the baseline) against the latest batch of predictions to detect data drift. This batch represents the most recent inference requests, allowing the monitoring service to compute statistical distribution differences (e.g., Jensen-Shannon divergence) and trigger alerts when drift exceeds a configured threshold. Using the latest batch ensures timely detection of shifts in the production data distribution.
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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