AI-102 Practice Question: Implement natural language processing solutions
Which TWO actions can you take to improve the performance of a Conversational Language Understanding model?
⚠ Common exam trap
Many candidates confuse 'reducing intents' with simplifying the model for better performance, but in CLU, performance is driven by data quality and evaluation-driven iteration, not by reducing complexity.
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
✓
Add more varied utterances to each intent.
Adding more varied utterances to each intent directly improves the model's ability to generalize and correctly classify user input by exposing it to a wider range of phrasing, synonyms, and sentence structures. This reduces overfitting to specific word patterns and increases the likelihood of accurate predictions on unseen data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add more varied utterances to each intent.
Why this is correct
More utterances improve accuracy.
- ✗
Reduce the number of intents.
Why it's wrong here
May not improve performance.
- ✓
Use the 'Evaluate' feature to review model predictions.
Why this is correct
Evaluate helps identify issues.
- ✗
Change the Azure region of the resource.
Why it's wrong here
Region does not affect model performance.
- ✗
Disable active learning.
Why it's wrong here
Active learning improves performance.
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Senior Network & Security Engineer · founder of Courseiva
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