AI-102 Plan and manage an Azure AI solution Practice Question
A developer is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The bot needs to handle multiple intents, including 'BookFlight', 'CancelFlight', and 'CheckWeather'. During testing, the bot frequently confuses 'BookFlight' and 'CancelFlight' intents. What is the most effective way to improve intent classification accuracy?
⚠ Common exam trap
It's easy for candidates to confuse confidence thresholds with model improvement, thinking that raising the threshold will fix misclassifications, when in reality it only masks the problem by rejecting more utterances instead of improving the model's discriminative power.
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 training utterances for 'BookFlight' and 'CancelFlight' intents.
Adding more varied training utterances for the 'BookFlight' and 'CancelFlight' intents directly addresses the root cause of confusion: insufficient or overlapping training data. LUIS relies on diverse utterance patterns to distinguish between semantically similar intents; increasing the quantity and variety of labeled examples improves the model's ability to learn discriminative features, thereby boosting classification accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of intents by merging similar ones.
Why it's wrong here
Merging intents may cause loss of functionality and does not improve accuracy.
- ✗
Increase the confidence threshold for intent predictions.
Why it's wrong here
Thresholds reduce false positives but do not improve model discrimination.
- ✗
Add more entities to the utterances.
Why it's wrong here
Entities are for extracting information, not for improving intent classification.
- ✓
Add more varied training utterances for 'BookFlight' and 'CancelFlight' intents.
Why this is correct
More diverse training data improves the model's ability to distinguish similar intents.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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