AI-102 Practice Question: Implement natural language processing solutions
You are building a conversational language understanding (CLU) project in Azure AI Language. You need to ensure the model can correctly interpret user utterances that include both an intent and multiple entities. Which two actions should you perform? (Choose two.)
⚠ Common exam trap
The trap here is assuming there is a project setting to enable multiple entity extraction or that intents should map to entity types, when the real work is in labeled data and evaluation.
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 utterances that contain multiple entities and label each entity with the correct entity type.
To handle utterances with multiple entities, you need labeled examples that include multiple entities with correct types, and you must train and evaluate the model to verify performance. Creating intents per entity, looking for a non-existent setting, or using prebuilt entities for custom types will not achieve the goal.
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 component for each custom entity to supplement training data.
Why it's wrong here
Prebuilt entities are for common types like datetime or number, not for arbitrary custom entities such as product names. Adding them does not replace the need for labeled custom entity examples. For custom entities, you must provide your own labeled utterances; prebuilt components are not a substitute.
- ✓
Add utterances that contain multiple entities and label each entity with the correct entity type.
Why this is correct
Labeling utterances with multiple entities teaches the model to recognize and extract each entity type in context. This is essential for multi-entity extraction because the model learns from examples where entities co-occur. Without such examples, the model may miss entities or confuse types, so this action directly supports the requirement.
- ✓
Train the model and review the evaluation metrics for entity precision and recall.
Why this is correct
After labeling, training and reviewing evaluation metrics for entities helps you identify gaps in multi-entity extraction. Precision and recall per entity indicate whether the model is missing or mislabeling entities. This iterative process is necessary to ensure the model meets the requirement, making it a valid action.
- ✗
Enable the "Extract multiple entities" option in the project settings.
Why it's wrong here
There is no such toggle in CLU project settings. Entity extraction is driven by labeled data and model training, not a global switch. Relying on a non-existent setting would not improve multi-entity recognition; proper labeling is the correct approach.
- ✗
Define a separate intent for each entity type to improve extraction accuracy.
Why it's wrong here
Intents represent the user's goal, not entity types. Creating an intent per entity type would fragment the intent model and confuse classification, as the same intent could contain different entities. Entities should be defined separately and labeled within utterances, not mapped to intents.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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