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PMLE Practice Question: A machine learning engineer is monitoring a…
A machine learning engineer is monitoring a deployed churn prediction model that has shown a gradual decline in accuracy over the past month. The engineer wants to diagnose the root cause of the performance degradation. Which TWO actions should the engineer take? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse reactive retraining (Option B) with diagnostic monitoring, failing to recognize that the first step in troubleshooting performance degradation is to identify the root cause through drift detection and ground truth comparison, not to immediately modify or retrain the model.
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
✓
Use Vertex AI Model Monitoring to detect data drift by comparing the distribution of recent input features against the training data distribution.
Option D is correct because Vertex AI Model Monitoring is designed to detect training-serving skew and prediction drift by comparing the statistical distribution of recent input features against the baseline training data distribution, which directly diagnoses whether the accuracy decline stems from data drift. Option E is correct because comparing recent predictions against newly collected ground truth labels measures actual model performance degradation and confirms whether the accuracy drop is real, providing the diagnostic evidence needed before remediation. Option A is wrong because increasing the learning rate and fine-tuning is a remediation action, not a diagnostic step, and could worsen the model without first identifying the root cause. Option B is wrong because immediately retraining on all historical data is a premature fix that does not diagnose the cause and may reintroduce stale patterns. Option C is wrong because deploying a parallel model for comparison does not identify the root cause of the existing model's degradation and adds operational complexity without diagnostic value.
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 the model's learning rate and fine-tune it on the latest data.
Why it's wrong here
Raising the learning rate and fine-tuning changes model parameters, which addresses neither data drift nor feature pipeline issues and can worsen accuracy. Learning-rate tuning is correct during training to improve convergence, not as a diagnostic action on a degraded production model.
- ✗
Immediately retrain the model using all available historical data to improve accuracy.
Why it's wrong here
Retraining immediately alters the model before the cause is known, destroying the evidence needed to diagnose drift and masking whether data, features or concept shift is responsible. Retraining is correct once root cause is confirmed and remediation is chosen, not as a diagnostic step.
- ✗
Deploy a second model in parallel to compare predictions.
Why it's wrong here
Deploying a parallel model compares two models, not the live model against current data, so it cannot reveal whether input distribution or label relationship has shifted. Shadow deployment is correct when validating a candidate model before promotion, not for diagnosing degradation of the existing one.
- ✓
Use Vertex AI Model Monitoring to detect data drift by comparing the distribution of recent input features against the training data distribution.
Why this is correct
Vertex AI Model Monitoring compares recent serving inputs against the training baseline, surfacing feature distribution shifts that explain gradual accuracy decay. This directly satisfies the stem's diagnostic goal: identifying data drift as the root cause of the churn model's month-long degradation, rather than retraining blindly.
- ✓
Monitor the model's prediction accuracy by comparing recent predictions against newly collected ground truth labels.
Why this is correct
Comparing recent predictions against newly collected ground truth labels directly quantifies the accuracy decline the stem describes, confirming whether degradation is real and ongoing. This satisfies the need to diagnose root cause by establishing the actual performance baseline, since ground truth reveals concept drift or data drift affecting the churn model's predictions over the past month.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.