AI0-001 AI Concepts and Foundations Practice Question
A small e-commerce startup has only 800 labeled customer-support tickets and needs to classify new tickets into categories such as billing, shipping, and returns. The team has no budget for large-scale annotation and wants to leverage a model already trained on millions of general text documents. Which approach best fits this constraint?
⚠ Common exam trap
The trap here is assuming that a small labeled dataset requires an unsupervised or rule-based workaround, when transfer learning from a pretrained model is specifically designed to succeed with limited task-specific labels.
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 pretrained language model on the 800 labeled tickets for the classification task.
Fine-tuning a pretrained language model is ideal when labeled data is scarce, because the model already encodes general language knowledge and only needs adaptation to the ticket categories. This approach uses the 800 labels efficiently and outperforms training from scratch, rule-based matching, or unsupervised clustering.
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 rule-based keyword matcher that assigns categories based on the presence of predefined terms.
Why it's wrong here
A keyword matcher is brittle and cannot handle paraphrases, context, or ambiguous tickets such as a billing complaint that mentions shipping. It requires manual rule maintenance and will misclassify as language varies. While it needs no training data, it ignores the available 800 labeled examples and the opportunity to use a pretrained model, so it underperforms the fine-tuning approach.
- ✗
Apply k-means clustering to the ticket text and label each cluster with the most frequent category.
Why it's wrong here
Clustering is unsupervised and does not use the 800 labels, so cluster boundaries will not align with the business categories. Assigning the most frequent category per cluster produces coarse, error-prone labels. This method discards supervision and cannot reliably separate billing from shipping tickets, making it a poor fit for the classification task.
- ✗
Train a transformer from scratch on the 800 tickets using a high learning rate.
Why it's wrong here
Training a transformer from scratch requires far more data than 800 examples; the model would overfit severely and fail to generalize. A high learning rate would further destabilize training. Without a large corpus, the model cannot learn useful language representations, so this approach wastes effort and produces poor classification accuracy for the support-ticket categories.
- ✓
Fine-tune a pretrained language model on the 800 labeled tickets for the classification task.
Why this is correct
Fine-tuning leverages representations learned from millions of general text documents, so the model already understands language structure and only needs task-specific adjustment. With 800 labeled examples, fine-tuning can achieve strong performance where training from scratch would fail. This approach fits the startup's limited annotation budget and directly addresses the multi-class ticket categorization need.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.