AI0-001 AI Concepts and Techniques Practice Question
A developer is building a natural language processing system to classify customer reviews as positive, neutral, or negative. They have 50,000 labeled reviews. Which model architecture is MOST appropriate for this task?
⚠ Common exam trap
A common mistake is to assume that training from scratch or using simpler models like logistic regression is sufficient, but pre-trained transformers like BERT are the standard for achieving high accuracy with limited labeled data.
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 a pre-trained BERT model
Fine-tuning a pre-trained BERT model is most appropriate because BERT is a transformer-based model pre-trained on a large corpus and can be fine-tuned on the 50,000 labeled reviews to achieve high accuracy with relatively little data. It captures bidirectional context, which is crucial for sentiment classification, and avoids the need for training from scratch.
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 a convolutional neural network (CNN) on raw text
Why it's wrong here
CNNs are typically used for image data; they can be applied to text but are less effective than transformers for this task.
- ✗
Train a recurrent neural network (RNN) from scratch
Why it's wrong here
RNNs struggle with long-range dependencies and are outperformed by transformers for text classification.
- ✓
Fine-tune a pre-trained BERT model
Why this is correct
BERT provides deep bidirectional representations; fine-tuning on the labeled reviews yields state-of-the-art text classification accuracy.
- ✗
Word2vec embeddings followed by logistic regression
Why it's wrong here
This approach works but lacks the contextual understanding of transformer models like BERT.
About these practice questions
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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.