AI0-001 AI Concepts and Techniques Practice Question
A team is deploying a sentiment analysis model for social media posts. The model currently performs well on English text but poorly on code-switched text (e.g., Spanglish). Which approach is MOST effective for improving performance on code-switched data without starting from scratch?
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 existing model on a corpus of code-switched text
Fine-tuning the existing model on a corpus of code-switched text adapts the model to the new language pattern efficiently.
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 larger base model without additional training
Why it's wrong here
A larger base model may still fail on unseen code-switched patterns without adaptation.
- ✗
Apply data augmentation by translating all code-switched posts to English
Why it's wrong here
Translation loses the code-switching pattern, defeating the purpose.
- ✗
Train a new model from scratch on a mix of English and code-switched data
Why it's wrong here
Training from scratch is resource-intensive and discards the existing model's learned features.
- ✓
Fine-tune the existing model on a corpus of code-switched text
Why this is correct
Fine-tuning leverages pre-trained knowledge and adapts to the target domain with less data and compute.
About these practice questions
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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.