AI0-001 AI Concepts and Techniques Practice Question
A team is using a pre-trained BERT model for a sentiment analysis task on product reviews. They want to adapt it to their specific domain with limited labeled data. Which approach is MOST effective?
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
✓
Fine-tune the pre-trained BERT model on the small labeled dataset
Fine-tuning the pre-trained model on the small labeled dataset is standard for transfer learning with BERT. Training from scratch would require massive data; feature extraction with a linear classifier is possible but less effective with limited data; data augmentation alone does not adapt the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use BERT as a feature extractor and train a logistic regression on top
Why it's wrong here
This works but fine-tuning typically yields better results with small datasets.
- ✗
Apply data augmentation to increase the dataset and then train from scratch
Why it's wrong here
Data augmentation helps but training from scratch is still inefficient.
- ✗
Train a new BERT model from scratch on the domain data
Why it's wrong here
Training from scratch requires huge amounts of data and compute.
- ✓
Fine-tune the pre-trained BERT model on the small labeled dataset
Why this is correct
Fine-tuning leverages pre-trained knowledge and adapts effectively with limited data.
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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.