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PDE Practice Question: Which THREE metrics should be monitored for a…
Which THREE metrics should be monitored for a deployed machine learning model in production?
⚠ Common exam trap
Google Cloud often tests the distinction between operational metrics (like latency, error rate, drift) and development/infrastructure metrics (like training time, replica count) to see if candidates understand what is relevant for ongoing model monitoring versus model building or deployment scaling.
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
✓
Prediction error rate
Prediction error rate (Option B) is a direct measure of model accuracy in production, reflecting how often the model's predictions deviate from actual outcomes. Monitoring this metric is essential for detecting model degradation, data quality issues, or concept drift that can silently reduce model performance over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Number of replicas
Why it's wrong here
Infrastructure metric, not model health.
- ✓
Prediction error rate
Why this is correct
Accuracy metric.
- ✓
Data drift detection
Why this is correct
Detects model degradation.
- ✗
Training time
Why it's wrong here
Training time is not monitored in production.
- ✓
Prediction latency
Why this is correct
Performance metric.
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