easyMultiple Choice
PDE Practice Question: Monitor the performance of a deployed model in…
A company wants to monitor the performance of a deployed model in production. Which metric indicates that the model's predictions are degrading?
⚠ Common exam trap
Google Cloud often tests the distinction between operational metrics (latency, throughput) and model performance metrics (error rate), trapping candidates who confuse system health with prediction quality.
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
✓
Increase in prediction error rate
An increase in prediction error rate directly indicates that the model's outputs are deviating from the expected or ground-truth values, signaling degradation in predictive performance. This metric captures the core concept of model drift, where the statistical properties of the input data or the relationship between features and labels change over time, leading to less accurate predictions. In production ML monitoring, tracking error rate (e.g., classification accuracy, RMSE) is the primary method to detect when a model needs retraining or updating.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase in prediction error rate
Why this is correct
A rising prediction error rate, measured against ground-truth labels or a proxy, directly signals that model accuracy is deteriorating in production. Other signals such as latency or throughput reflect infrastructure health, not predictive quality, so they cannot indicate degradation of the model itself.
- ✗
Increase in prediction latency
Why it's wrong here
Latency measures serving speed, not predictive accuracy; a model can answer quickly while its outputs drift from ground truth. It is tempting because latency spikes often accompany overload, but degradation is detected through accuracy, precision, recall or drift metrics against labelled outcomes.
- ✗
Decrease in throughput
Why it's wrong here
Throughput reflects request volume handled per unit time, an infrastructure capacity measure unrelated to prediction correctness. It is tempting because falling throughput can signal saturation, yet a model may serve fewer requests while remaining accurate; accuracy or drift metrics reveal degradation.
- ✗
Increase in number of requests
Why it's wrong here
Request volume measures load, not predictive quality; accuracy, precision, recall or drift metrics reveal degradation. It is tempting because rising requests often accompany scaling or incidents, so it appears correlated with problems, but it would be the correct signal for capacity planning or autoscaling rather than model performance monitoring.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PDE question from scratch — 747 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.