AI0-001 Implementing AI Solutions Practice Question
A team is training a image classification model. They split the dataset into training, validation, and test sets. After training, the model achieves 98% accuracy on the training set but only 72% on the test set. Which step in the AI project lifecycle should the team focus on?
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
✓
Model selection – use regularization or reduce model complexity
The large gap indicates overfitting, which is a model selection/regularization issue. They need to apply techniques like dropout, data augmentation, or reduce model complexity.
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 acquisition – collect more data
Why it's wrong here
While more data can help, the immediate problem is overfitting to the training data, not insufficient data quantity.
- ✓
Model selection – use regularization or reduce model complexity
Why this is correct
The high training accuracy and low test accuracy is classic overfitting. Regularization, dropout, or simpler models can reduce the gap.
- ✗
Deployment – re-deploy with a different serving framework
Why it's wrong here
Deployment is not related to model overfitting; the issue is in the training phase.
- ✗
Data preparation – check for train/test leakage
Why it's wrong here
Train/test leakage would cause both training and test performance to be high; here the test performance is low, so leakage is not indicated.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.