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PDE Practice Question: Implement model monitoring for a deployed…
A company wants to implement model monitoring for a deployed classification model. Which three types of monitoring should they set up? (Select 3)
⚠ Common exam trap
Candidates often confuse operational tasks (like cost monitoring or version management) with model monitoring, leading them to incorrectly select options like infrastructure cost monitoring or model version comparison.
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
✓
Training-serving skew
For a deployed classification model, the three essential monitoring types are training-serving skew (B), prediction drift (C), and input feature drift (D). Training-serving skew (B) is correct because it detects discrepancies between the feature transformations applied during training and those applied at inference time, which can silently degrade model accuracy in production. Prediction drift (C) is correct because it tracks changes in the distribution of the model's output predictions over time, signaling that the model's behavior is shifting even when ground truth labels are unavailable. Input feature drift (D) is correct because it monitors changes in the distribution of incoming feature values relative to the training data, which is often an early warning that the model is operating on data unlike what it learned from. Infrastructure cost monitoring (A) is not a model-quality monitoring type and does not detect model degradation, and model version comparison (E) is a deployment/management activity rather than an ongoing monitoring category for a deployed classifier.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Infrastructure cost monitoring
Why it's wrong here
Cost monitoring tracks spend on compute and storage, not whether predictions stay accurate or inputs drift. It is genuinely useful for budget alerts and capacity planning, but a classification model's health depends on data drift, feature skew and performance metrics, none of which cost telemetry captures.
- ✓
Training-serving skew
Why this is correct
Training-serving skew monitoring compares the feature distributions your model saw during training against those arriving at prediction time, exposing preprocessing mismatches. Vertex AI Model Monitoring supports this as a distinct skew type, satisfying the requirement to detect divergence between training and serving data.
- ✓
Prediction drift
Why this is correct
Prediction drift monitoring tracks changes in the distribution of the model's output predictions over time, flagging shifts in what the deployed classifier actually returns. Vertex AI Model Monitoring offers this as a supported monitoring type, satisfying the need to detect output-side degradation.
- ✓
Input feature drift
Why this is correct
Input feature drift monitoring detects changes in the distribution of incoming request features relative to a baseline, independent of training data. Vertex AI Model Monitoring supports this type, satisfying the requirement to catch evolving production inputs before they degrade classification accuracy.
- ✗
Model version comparison
Why it's wrong here
Version comparison contrasts two registered model iterations; it cannot detect live degradation in the deployed endpoint. It belongs in retraining and release governance, where you assess whether a candidate outperforms the incumbent before promotion, not in ongoing production monitoring of drift, data quality and performance.
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