AI0-001 Machine Learning and Deep Learning Practice Question
A hospital wants to deploy a machine learning model to predict patient readmission risk within 30 days. They have a dataset with 10,000 records, 70 features including demographics, lab results, and past admissions. The target variable is binary (readmitted or not). The data scientist trains a logistic regression model and achieves an AUC of 0.85 on the test set. However, the hospital's clinicians require interpretability of predictions to trust the model. Which action should the data scientist take to ensure the model meets the interpretability requirement while maintaining performance?
⚠ Common exam trap
AI0-001 often tests the misconception that more complex models with post-hoc explanation tools (like SHAP or LIME) are necessary for interpretability, when in fact inherently interpretable models like logistic regression should be preferred when they meet performance requirements.
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 the logistic regression model as is, since it is inherently interpretable with coefficients
Logistic regression is a linear model whose predictions are computed as a weighted sum of input features passed through a sigmoid function. The coefficients directly represent the log-odds change per unit increase in each feature, making the model inherently interpretable without any post-hoc explanation tools. Since the model already achieves an AUC of 0.85, which meets performance requirements, no architectural change is needed. The data scientist should retain the logistic regression model and present the coefficients (and odds ratios) to clinicians to satisfy the interpretability requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of features to 10 using PCA and retrain the logistic regression
Why it's wrong here
PCA produces orthogonal principal components that are linear combinations of the original 70 features, so coefficients no longer map to interpretable clinical variables such as lab results or prior admissions. It is tempting as dimensionality reduction, but it destroys the direct feature-to-prediction link clinicians require.
- ✗
Replace logistic regression with a random forest model and use feature importance plots
Why it's wrong here
Random forest feature importance plots show global, aggregate rankings, not the per-patient reasoning clinicians need to justify individual readmission predictions. It is tempting because random forests often match or exceed logistic regression AUC, but they sacrifice the coefficient-level transparency that makes logistic regression auditable at the point of care.
- ✗
Train a deep neural network and apply LIME or SHAP for explanations
Why it's wrong here
A deep neural network sacrifices the logistic regression's inherent interpretability, and post-hoc LIME or SHAP approximations do not satisfy clinicians needing transparent, directly explainable predictions. It is tempting when accuracy is paramount, but it would be correct only if the requirement were raw predictive performance rather than interpretability.
- ✓
Use the logistic regression model as is, since it is inherently interpretable with coefficients
Why this is correct
Logistic regression produces coefficients that quantify each feature's contribution to the predicted readmission probability, satisfying the clinicians' interpretability constraint directly. Its AUC of 0.85 already meets performance expectations, so no trade-off or surrogate explainability tooling is needed.
About these practice questions
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.