AI0-001 AI Concepts and Foundations Practice Question
A hospital uses an AI system to predict patient deterioration from vital signs. The system currently uses a logistic regression model trained on data from the past year. Recently, the hospital adopted a new patient monitoring device that provides more accurate readings. The model's performance has dropped significantly. The data science team has access to the new device's data for the past month and wants to improve the model with minimal disruption. The team also wants to ensure the model remains interpretable for regulatory compliance. Which approach should they take?
⚠ Common exam trap
CompTIA often tests the trade-off between model performance and interpretability, and the trap here is that candidates may prioritize performance gains from complex models (like gradient boosting or neural networks) without recognizing that regulatory compliance mandates interpretability, making logistic regression the only viable choice despite its simplicity.
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
✓
Retrain the logistic regression model on a combined dataset of old and new device data
Retraining the logistic regression model on a combined dataset of old and new device data is the best approach because it leverages all available data to adapt the model to the new device's measurement distribution while preserving the model's inherent interpretability. Logistic regression is a linear model that remains fully transparent for regulatory compliance, and combining both datasets helps the model learn the systematic shift in vital sign readings without discarding valuable historical patterns. This minimizes disruption by avoiding a complete overhaul and directly addresses the performance drop caused by the change in input data distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrain the logistic regression model on a combined dataset of old and new device data
Why this is correct
Retraining the logistic regression on combined old and new device data adapts the model to the new readings while retaining its interpretable, coefficient-based form, satisfying the regulatory requirement. More complex models would improve accuracy but sacrifice the interpretability compliance demands.
- ✗
Continue using the current model and manually adjust predictions based on device differences
Why it's wrong here
Manual adjustment leaves the model itself unretrained on the new device's readings, so predictions stay systematically skewed and unauditable at scale. Human override suits rare edge cases, not a sustained distribution shift affecting every vital-sign input.
- ✗
Build an ensemble of logistic regression and a neural network using new data only
Why it's wrong here
Adding a neural network destroys the interpretability regulators require, and training on one month of new-device data alone discards the year of history. Ensembles suit accuracy gains where explainability is not mandated; here logistic regression must remain the model.
- ✗
Replace the logistic regression model with a gradient boosting model using only new device data
Why it's wrong here
Gradient boosting sacrifices the linear interpretability regulators demand, and using only new-device data discards the prior year, worsening overfitting on one month. Boosting suits accuracy-first problems without explainability constraints; this scenario requires both interpretability and retained history.
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