AI0-001 Implementing AI Solutions Practice Question
A data scientist is training a binary classifier and observes that the training accuracy is 99% but the test accuracy is only 70%. Which of the following is the MOST likely cause?
⚠ Common exam trap
AI0-001 often tests the train-vs-test accuracy gap pattern, and candidates confuse overfitting (high train, low test) with underfitting (low train, low test) or with data leakage (which inflates test scores).
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
✓
The model is overfitting the training data
A large gap between training accuracy (99%) and test accuracy (70%) is the classic signature of overfitting: the model has memorized the training data, including its noise, and therefore fails to generalize to unseen examples. The high training score confirms the model has enough capacity to fit the training set, while the poor test score shows that capacity is being used for memorization rather than learning generalizable patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is underfitting the training data
Why it's wrong here
Underfitting produces low training accuracy as well, contradicting the observed 99%. It is tempting because it is a standard diagnosis for poor generalisation, and it would be correct if the model scored badly on both training and test data.
- ✗
The learning rate is too high
Why it's wrong here
An excessive learning rate causes divergence or oscillation, typically leaving training accuracy low too, not 99%. It is tempting because learning rate is a common tuning culprit, and it would be correct if both training and validation loss failed to decrease.
- ✓
The model is overfitting the training data
Why this is correct
A 29-point gap between training and test accuracy is the classic signature of overfitting: the model has memorised training noise rather than learning generalisable patterns. The constraint in the stem is the large train-test performance divergence, which variance-reduction techniques such as regularisation or more data would address.
- ✗
The test set contains data leakage from the training set
Why it's wrong here
Leakage inflates test accuracy, not depresses it, so it cannot explain a 30-point train-test gap. It is tempting because leakage is a genuine evaluation flaw, and it would be the answer if test scores were unrealistically high rather than low.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.