AI-102 Practice Question: Implement natural language processing solutions
You are developing a conversational language understanding (CLU) project in Azure AI Language. You have defined intents and entities for a banking bot. During testing, you notice that utterances containing the phrase 'transfer funds' are sometimes predicted as the 'CheckBalance' intent instead of 'TransferMoney'. You have already added 20 labeled examples for 'TransferMoney' and 20 for 'CheckBalance'. What should you do to improve the model's ability to distinguish between these intents?
⚠ Common exam trap
The trap here is thinking that more training epochs or additional entities will resolve intent confusion, but the real issue is the diversity and quantity of labeled utterances for the problematic intent.
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 labeled utterances for 'TransferMoney' that include variations of the phrase 'transfer funds' and similar expressions, ensuring diversity in phrasing.
The model misclassifies utterances because the training data for 'TransferMoney' may not include enough variations of the phrase 'transfer funds'. Adding more diverse labeled examples for that intent helps the model learn the distinguishing features. Increasing epochs or adding entities does not directly address intent confusion. Multilingual settings are irrelevant here.
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 a prebuilt entity for 'money' to the project and map it to the 'TransferMoney' intent.
Why it's wrong here
Prebuilt entities extract specific data types like amounts, but they do not influence intent classification. The misclassification is at the intent level, not entity extraction. Adding a prebuilt entity won't help the model differentiate between 'TransferMoney' and 'CheckBalance' intents. The root cause is insufficient training examples for the intents.
- ✓
Add more labeled utterances for 'TransferMoney' that include variations of the phrase 'transfer funds' and similar expressions, ensuring diversity in phrasing.
Why this is correct
This is correct because adding more diverse labeled examples for the confusing intent helps the model learn the distinguishing features. The issue is likely due to insufficient or non-diverse training data for 'TransferMoney', causing the model to misclassify ambiguous utterances. By providing varied examples that include the problematic phrase and its synonyms, the model can better generalize.
- ✗
Enable the 'Use multiple languages' option in the project settings to improve model understanding.
Why it's wrong here
The multiple languages option is for supporting utterances in multiple languages; it does not improve intent classification for a single language. The scenario does not mention multilingual requirements. Enabling this option would not address the confusion between intents. The correct action is to enhance training data for the affected intent.
- ✗
Increase the number of training epochs for the CLU model to allow it to learn more from the existing data.
Why it's wrong here
Increasing training epochs may lead to overfitting rather than improving distinction between intents. If the training data lacks diversity or representative examples, more epochs won't help the model learn new patterns. The problem is data quality and coverage, not training duration. Azure AI Language's CLU training automatically determines epochs; manual tuning is not exposed.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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