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AIF-C01 Practice Question: An ML engineer is training a linear regression…
An ML engineer is training a linear regression model and notices that adding more features increases training error. What is the most likely cause?
⚠ Common exam trap
The AWS AI Practitioner exam often tests the misconception that adding features always reduces training error due to overfitting, but the trap here is that irrelevant features can actually increase training error by introducing noise that the model cannot ignore.
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
✓
Irrelevant features
Adding more features increases training error because irrelevant features introduce noise that the model tries to fit, degrading its ability to capture the true underlying pattern. In linear regression, irrelevant features can cause the model to learn spurious correlations, increasing the residual sum of squares (RSS) on the training data. This is a classic sign of feature pollution, not overfitting or underfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Irrelevant features
Why this is correct
Adding irrelevant features can introduce noise and increase training error in linear regression.
- ✗
Underfitting
Why it's wrong here
Underfitting would show high error regardless of feature count; adding features might help.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity affects coefficient stability but doesn't always increase training error.
- ✗
Overfitting
Why it's wrong here
Overfitting typically reduces training error while increasing validation error.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.