AI Associate AI Fundamentals Practice Question
A data scientist notices that a sentiment analysis model performs well on general product reviews but fails to correctly classify negative sentiment in industry-specific jargon (e.g., 'the API is flaky'). The most likely cause is:
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
✓
The training data lacked examples of industry-specific language
The model was likely trained on general data and does not generalize to domain-specific language. This is a domain adaptation issue, which can be due to training data not being representative.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The training data lacked examples of industry-specific language
Why this is correct
Correct: if training data does not include domain terms, the model cannot learn them.
- ✗
The model is overfitted to training data
Why it's wrong here
Overfitting would cause poor performance on any data not seen, but the issue here is domain-specific terms.
- ✗
The model architecture is too simple
Why it's wrong here
Architecture might matter, but the core issue is lack of representative training examples.
- ✗
Sentiment analysis cannot handle jargon
Why it's wrong here
Sentiment analysis can handle jargon if training data includes it.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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