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PMLE Practice Question: A financial services company uses a custom deep…
A financial services company uses a custom deep learning model on Vertex AI to automatically approve or reject credit card transactions. The model is explainable using Vertex Explainable AI, and the company monitors feature attribution drift with thresholds defined per feature. Last week, the monitoring system flagged that the mean absolute attribution score for the 'transaction_amount' feature increased from 0.35 to 0.55. The overall model accuracy, measured on a daily batch of labeled transactions, has remained around 97%. The operations team is concerned about potential compliance issues due to changing model behavior. What should the data scientist do?
⚠ Common exam trap
A common mix-up: candidates assume stable accuracy means the model is fine, but the PMLE exam tests that feature attribution drift can indicate a change in model behavior that accuracy alone cannot detect, especially for compliance-sensitive applications.
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
✓
Investigate whether there has been a shift in the distribution of 'transaction_amount' values in the recent transaction data, which could explain the attribution change.
A shift in the distribution of the 'transaction_amount' feature (e.g., due to seasonality or a new customer segment) can naturally cause its attribution score to change without indicating model degradation. Vertex Explainable AI computes feature attributions relative to the current data distribution; if the input values shift, the model's reliance on that feature may legitimately increase. Investigating the distribution shift is the first diagnostic step before adjusting thresholds or retraining, as stable accuracy does not rule out data drift that could lead to compliance issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Tune the alert threshold for 'transaction_amount' to 0.6 to avoid future false alarms.
Why it's wrong here
Raising the threshold hides the drift signal rather than investigating it, so the compliance concern remains unaddressed. It tempts because the alert may look like noise given 97% accuracy, and would be correct only after analysis confirms the threshold was genuinely misconfigured.
- ✗
Retrain the model by increasing regularization to reduce the importance of the 'transaction_amount' feature.
Why it's wrong here
Increasing regularisation alters learned weights to suppress a feature, changing model behaviour without diagnosing why attribution rose, and risks degrading accuracy. It tempts as a quick way to dampen feature influence, and would be correct if the feature were proven noisy or leaking.
- ✓
Investigate whether there has been a shift in the distribution of 'transaction_amount' values in the recent transaction data, which could explain the attribution change.
Why this is correct
Rising mean absolute attribution for transaction_amount with stable accuracy suggests the feature's values themselves have shifted, changing its influence. Checking the recent distribution confirms whether input drift explains the attribution change, satisfying the compliance concern about altered model behaviour.
- ✗
Disable the feature attribution drift monitoring for 'transaction_amount' since the model accuracy is stable.
Why it's wrong here
Disabling monitoring removes the compliance evidence the operations team needs, and stable accuracy does not explain the attribution shift. It tempts because accuracy is the headline metric, and would be correct only where a feature's attribution is genuinely irrelevant and no regulatory explainability obligation applies.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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