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
This incorporates the new device's accuracy while maintaining interpretability and using all available data.
- ✗
Continue using the current model and manually adjust predictions based on device differences
Why it's wrong here
Manual adjustments are not scalable and may introduce inconsistency.
- ✗
Build an ensemble of logistic regression and a neural network using new data only
Why it's wrong here
An ensemble reduces interpretability and complexity; using only new data may not be robust.
- ✗
Replace the logistic regression model with a gradient boosting model using only new device data
Why it's wrong here
Gradient boosting is less interpretable, and using only one month of data may not be sufficient.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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