AI-102 Practice Question: Implement natural language processing solutions
You are deploying a conversational language understanding project in Azure AI Language for a banking chatbot. Testing shows the model frequently confuses the intents TransferFunds and PayBill because both utterances contain similar wording about moving money. You need to improve the model's ability to distinguish these two intents. What should you do?
⚠ Common exam trap
The trap here is reaching for a confidence threshold change to suppress wrong predictions, when the real fix for two confusable intents is more discriminative labeled utterances.
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 that are representative of each intent, including boundary examples that clarify the difference between them
When two intents overlap in wording, the model needs more and better-labeled examples that highlight their distinguishing characteristics. Adding representative and boundary utterances for both intents gives the training algorithm the discriminative signal required to separate them. Adjusting confidence thresholds, merging intents, or adding entities that appear in both classes does not address the underlying classification boundary.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the model's confidence threshold so low-confidence predictions are rejected instead of misclassified
Why it's wrong here
Raising the confidence threshold causes low-confidence predictions to fall back to the None intent, but it does not teach the model to distinguish the two intents. Utterances that are genuinely ambiguous will simply be rejected rather than correctly routed, degrading the user experience. Threshold tuning is a guardrail for uncertain inputs, not a remedy for systematic confusion between two well-represented intents.
- ✗
Merge the two intents into a single intent and let the bot ask a follow-up question to determine the action
Why it's wrong here
Merging the intents removes the very distinction the model must learn and pushes disambiguation onto the user through an extra turn. That changes the conversational design rather than improving the model, and it may not be acceptable if downstream systems require the intent to be identified in one step. It also discards the labeling work already done for both intents.
- ✓
Add more labeled utterances that are representative of each intent, including boundary examples that clarify the difference between them
Why this is correct
Intent confusion between semantically close classes is resolved by increasing and diversifying labeled utterances for both intents, especially examples near the decision boundary. Adding utterances that emphasize the distinguishing features, such as payee type or timing, gives the model the signal it needs to separate the classes. This directly targets the observed confusion rather than changing unrelated configuration.
- ✗
Add a prebuilt entity such as Money to both intents so the model can use amounts to tell them apart
Why it's wrong here
Both TransferFunds and PayBill utterances commonly contain monetary amounts, so a Money entity does not separate them and may even increase overlap. Entities capture slot values for fulfillment; they do not directly resolve intent classification confusion. Relying on an entity that appears in both classes adds noise rather than discriminative signal, so it will not improve the model's ability to choose between the two intents.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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