AI Associate AI Fundamentals Practice Question
A data scientist trains a model to predict customer churn. The model achieves 98% accuracy on training data but only 72% on test data. What issue is most likely occurring?
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
Overfitting occurs when a model learns training data too well, including noise, and fails to generalize to new 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.
- ✗
Data leakage
Why it's wrong here
Data leakage often causes overly optimistic performance, but not necessarily a large gap.
- ✗
Underfitting
Why it's wrong here
Underfitting is incorrect because it describes a model that is too simplistic, resulting in poor performance on *both* training and test datasets. The scenario's high training accuracy (98%) directly contradicts this, indicating the model *has* learnt the training data well. This option is tempting as underfitting is another common model performance problem, and it would be the correct choice if the model exhibited low accuracy on both training and test data.
- ✓
Overfitting
Why this is correct
The large gap between training and test accuracy indicates overfitting.
- ✗
Bias in the training data
Why it's wrong here
Bias typically causes systematic errors, not a large accuracy gap.
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Same concept, more angles
2 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist trains a model to predict customer churn. The model performs well on training data but poorly on test data. Which TWO issues are most likely?
medium- ✓ A.Overfitting
- B.Insufficient training data
- C.High bias
- D.Underfitting
- ✓ E.Data leakage
Why A: Overfitting means the model memorized training data and fails to generalize. Data leakage inflates training performance but not test performance.
Variation 2. A data scientist trains a churn prediction model on CRM data that includes customer tenure, support ticket count, and last purchase date. The model achieves 95% accuracy on training data but only 60% on a holdout validation set. What is the most likely issue?
hard- A.The validation set contains data leakage
- B.The model needs more features
- ✓ C.The model is overfitting the training data
- D.The model is underfitting the training data
Why C: Large gap between training accuracy and validation accuracy is a classic sign of overfitting. The model memorized training data and fails to generalize.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.