easyMultiple Choice
AIF-C01 Practice Question: An ML team notices that the training accuracy is…
An ML team notices that the training accuracy is 99% but validation accuracy is only 72%. Which concept best describes this situation?
⚠ Common exam trap
AWS often tests the distinction between overfitting and the bias-variance tradeoff, where candidates may confuse the tradeoff as the direct answer instead of recognizing that the specific symptom (high training accuracy, low validation accuracy) is the definition of 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 model achieves 99% accuracy on training data but only 72% on validation data, which is a classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set too well, failing to generalize to unseen data. This is often caused by excessive model complexity, such as too many layers in a neural network or too deep a decision tree, relative to the amount of training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cross-validation error
Why it's wrong here
Cross-validation error is the averaged error across validation folds used to estimate generalisation, not the divergence between training and validation accuracy. It is tempting because cross-validation is the correct technique when selecting hyperparameters or comparing candidate models on limited data.
- ✓
Overfitting
Why this is correct
Overfitting occurs when a model memorises training data, capturing noise rather than generalisable patterns, so training accuracy stays high while validation accuracy lags. The 99% versus 72% gap directly satisfies the stem's constraint: a large, persistent divergence between training and validation performance on unseen data.
- ✗
Bias-variance tradeoff
Why it's wrong here
Bias-variance tradeoff describes the general balance between model complexity and generalisation error, not the specific 99% versus 72% gap. It is tempting because the tradeoff is the correct concept when tuning complexity to minimise both underfitting and overfitting simultaneously.
- ✗
Underfitting
Why it's wrong here
Underfitting means both training and validation accuracy are poor, which contradicts the 99% training result. It is tempting because underfitting is the correct diagnosis when a model is too simple to capture the underlying pattern and performs badly on the training set as well.
Go deeper
Related to this question
About these practice questions
One of 862 original AIF-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.