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
A CNN on raw text learns local n-gram features but ignores long-range dependencies and typically underperforms transformer models on sentiment tasks. It is tempting because CNNs are fast and effective for short-text classification, and would be correct for keyword-driven tasks such as topic detection.
- ✗
Train a recurrent neural network (RNN) from scratch
Why it's wrong here
An RNN trained from scratch learns sequential context but needs far more labelled data and compute to reach comparable accuracy on sentiment classification. It is tempting because RNNs handle word order, and would be correct for tasks needing long-range sequential dependencies, such as machine translation.
- ✓
Fine-tune a pre-trained BERT model
Why this is correct
Fine-tuning a pre-trained BERT model leverages transformer self-attention and language representations learned from vast corpora, then adapts them to three-class review sentiment using the 50,000 labelled examples, yielding strong accuracy where training a model from scratch would underperform.
- ✗
Word2vec embeddings followed by logistic regression
Why it's wrong here
Word2vec produces static, context-independent embeddings, so a logistic regression layer cannot capture word order or polysemy in review text. It is tempting because it is a fast, low-cost baseline, and would be correct when labelled data is scarce and a quick benchmark is needed.
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.