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 still lacks code-switched training signal, so its extra parameters do not teach it Spanglish sentiment boundaries. It is tempting because scaling often improves generalisation on in-distribution text, and it would be the right choice when the bottleneck is model capacity rather than a domain or language mismatch in the training data.
- ✗
Apply data augmentation by translating all code-switched posts to English
Why it's wrong here
Translating code-switched posts to English discards the intra-sentential language mixing that carries sentiment cues, and translation errors distort labels. Augmentation is correct for expanding scarce labelled data, but translation removes the very phenomenon the model must learn.
- ✗
Train a new model from scratch on a mix of English and code-switched data
Why it's wrong here
Training from scratch discards the existing English model's learned representations and demands far more labelled code-switched data than fine-tuning needs. It is tempting because large mixed corpora can yield strong multilingual models, but that approach suits greenfield projects where no usable baseline exists, not this scenario's explicit 'without starting from scratch' constraint.
- ✓
Fine-tune the existing model on a corpus of code-switched text
Why this is correct
Fine-tuning adapts the existing model's weights to code-switched patterns, satisfying the requirement to avoid training from scratch. It teaches the model to handle intra-sentence language mixing, which the original English-only training data never exposed it to.
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.