- A
Reduce the number of features to 10 using PCA and retrain the logistic regression
Why wrong: PCA reduces dimensionality but loses feature interpretability and may hurt performance.
- B
Replace logistic regression with a random forest model and use feature importance plots
Why wrong: Random forest is an ensemble and less interpretable than logistic regression.
- C
Train a deep neural network and apply LIME or SHAP for explanations
Why wrong: Deep networks are black boxes; LIME/SHAP add explanations but reduce inherent interpretability.
- D
Use the logistic regression model as is, since it is inherently interpretable with coefficients
Logistic regression coefficients provide direct interpretability for each feature.
Question 302 of 1,000
Achieving Model Interpretability with Logistic Regression
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Quick Answer
The answer is to use the logistic regression model as is, since it is inherently interpretable through its coefficients. This is correct because logistic regression provides direct, transparent insight into how each feature influences the predicted probability of readmission—each coefficient represents the log-odds change for a one-unit increase in that feature, holding others constant. On the CompTIA AI+ AI0-001 exam, this question tests the critical distinction between model performance and interpretability, a key trade-off in healthcare AI. A common trap is assuming you must add post-hoc explainability tools like LIME or SHAP, but the exam emphasizes that logistic regression itself already satisfies interpretability requirements without sacrificing the 0.85 AUC. Remember the mnemonic: “Coefficients are the keys to clarity”—if a model’s weights are directly readable, you don’t need extra layers of explanation.
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
Option A (PCA + logistic regression) reduces dimensionality but loses interpretability and may degrade performance. Option B (random forest with feature importance) is less interpretable than logistic regression. Option C (deep neural network with LIME/SHAP) adds complexity and may reduce transparency. Option D (keep logistic regression) provides inherent interpretability through coefficients, meeting the requirement without sacrificing performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 reduces dimensionality but loses feature interpretability and may hurt performance.
- ✗
Replace logistic regression with a random forest model and use feature importance plots
Why it's wrong here
Random forest is an ensemble and less interpretable than logistic regression.
- ✗
Train a deep neural network and apply LIME or SHAP for explanations
Why it's wrong here
Deep networks are black boxes; LIME/SHAP add explanations but reduce inherent interpretability.
- ✓
Use the logistic regression model as is, since it is inherently interpretable with coefficients
Why this is correct
Logistic regression coefficients provide direct interpretability for each feature.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
Machine Learning and Deep Learning — study guide chapter
Learn the concepts, then practise the questions
- →
Machine Learning and Deep Learning practice questions
Targeted practice on this topic area only
- →
All AI0-001 questions
1,000 questions across all exam domains
- →
CompTIA AI+ AI0-001 study guide
Full concept coverage aligned to exam objectives
- →
AI0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI0-001 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
AI Infrastructure and Technologies practice questions
Practise AI0-001 questions linked to AI Infrastructure and Technologies.
AI Security practice questions
Practise AI0-001 questions linked to AI Security.
AI Concepts and Foundations practice questions
Practise AI0-001 questions linked to AI Concepts and Foundations.
AI Concepts and Techniques practice questions
Practise AI0-001 questions linked to AI Concepts and Techniques.
Machine Learning and Deep Learning practice questions
Practise AI0-001 questions linked to Machine Learning and Deep Learning.
AI Models and Data Engineering practice questions
Practise AI0-001 questions linked to AI Models and Data Engineering.
Implementing AI Solutions practice questions
Practise AI0-001 questions linked to Implementing AI Solutions.
AI Implementation and Operations practice questions
Practise AI0-001 questions linked to AI Implementation and Operations.
AI Security, Ethics and Governance practice questions
Practise AI0-001 questions linked to AI Security, Ethics and Governance.
AI Governance and Ethics practice questions
Practise AI0-001 questions linked to AI Governance and Ethics.
CompTIA A+ hardware practice questions
Practise AI0-001 questions linked to CompTIA A+ hardware.
CompTIA A+ mobile devices practice questions
Practise AI0-001 questions linked to CompTIA A+ mobile devices.
Practice this exam
Start a free AI0-001 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the logistic regression model as is, since it is inherently interpretable with coefficients — Option A (PCA + logistic regression) reduces dimensionality but loses interpretability and may degrade performance. Option B (random forest with feature importance) is less interpretable than logistic regression. Option C (deep neural network with LIME/SHAP) adds complexity and may reduce transparency. Option D (keep logistic regression) provides inherent interpretability through coefficients, meeting the requirement without sacrificing performance.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More AI0-001 practice questions
- A data science team uses a CI/CD pipeline for ML models. They need to ensure that each model version is traceable back t…
- A company is deploying a large language model for customer support. They want to reduce the number of off-topic or nonse…
- A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model…
- A team is designing an AI system for autonomous driving. They need to decide between an end-to-end deep learning approac…
- A team is using Kubeflow to orchestrate ML workflows on Kubernetes. They need to ensure reproducibility, track experimen…
- A healthcare organization deploys an AI system to analyze medical images and detect anomalies. During a routine audit, t…
Last reviewed: Jun 23, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.