AI0-001 Machine Learning and Deep Learning Practice Question
A machine learning team is deploying a model that predicts customer churn. They notice that the model's predictions are highly sensitive to small changes in input features, leading to inconsistent outputs. Which technique should the team apply to improve model stability?
⚠ Common exam trap
CompTIA often tests the misconception that feature scaling alone can fix model instability, but scaling only normalizes inputs and does not penalize large weights, which is the root cause of sensitivity to small input changes.
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
✓
Regularization
Regularization (Option C) is the correct technique because it adds a penalty term to the loss function (e.g., L1 or L2 regularization), which constrains the model's weights. This reduces variance and prevents overfitting to noise in the training data, directly addressing the high sensitivity to small input changes (brittleness). By shrinking coefficients, regularization forces the model to learn more general patterns, improving stability and consistency in predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase learning rate
Why it's wrong here
Increasing the learning rate can lead to divergence and instability during training.
- ✗
Feature scaling
Why it's wrong here
Feature scaling normalizes input ranges but does not directly address sensitivity to small changes.
- ✓
Regularization
Why this is correct
Regularization adds a penalty for large weights, reducing overfitting and sensitivity to input variations.
- ✗
Cross-validation
Why it's wrong here
Cross-validation is used to assess model performance, not to improve prediction stability.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.