easyMultiple Choice
AIF-C01 Practice Question: A data scientist trains a linear regression model…
A data scientist trains a linear regression model to predict housing prices. The model achieves a low training error but a high test error. Which concept does this BEST illustrate?
⚠ Common exam trap
AWS often tests the distinction between overfitting and the bias-variance tradeoff, where candidates mistakenly select 'bias-variance tradeoff' because they recognize high variance, but the question explicitly asks for the concept best illustrated by the specific error pattern, which is overfitting.
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
✓
Overfitting
The scenario describes a model that performs well on training data but poorly on unseen test data, which is the classic definition of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than the underlying generalizable relationship, leading to high variance and poor test performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bias-variance tradeoff
Why it's wrong here
The bias-variance tradeoff describes the general tension between underfitting and overfitting, not the specific diagnosis of a model that fits training data well and generalises poorly. It is tempting as the umbrella concept, but the stem's low training error and high test error names variance directly.
- ✗
Regularization
Why it's wrong here
Regularization deliberately penalises model complexity to reduce variance, so it would lower the gap rather than cause it. It is tempting because it addresses overfitting, but the stem asks which concept the observed behaviour illustrates, and no penalty term is described in the training process.
- ✗
Underfitting
Why it's wrong here
Underfitting produces high error on both training and test data, because the model is too simple to capture the underlying pattern. It is tempting as the opposite failure mode, but the stem's low training error with high test error indicates the model memorised the training set.
- ✓
Overfitting
Why this is correct
Low training error with high test error means the model has memorised training noise rather than learning generalisable patterns, so it fails on unseen data. Overfitting is the specific term for this train-test performance gap, which the stem describes directly.
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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.