AI-102 Practice Question: Implement natural language processing solutions
A company uses Azure AI Language to analyze customer call transcripts. They need to identify specific entities such as product names and issue types. The prebuilt entity recognition does not cover their custom entities. Which approach should they take to extract both standard and custom entities from the transcripts?
⚠ Common exam trap
It's easy for candidates to assume a single CLU project (Option B) can handle both standard and custom entities, but CLU is optimized for conversational flows and does not include prebuilt entity recognition for standard types like dates or numbers.
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
✓
Use a custom named entity recognition (NER) model for custom entities and call the prebuilt entity recognition API separately for standard entities.
Azure AI Language provides separate APIs for prebuilt entity recognition (covering standard entities like dates, numbers, and common types) and custom named entity recognition (NER) for domain-specific entities like product names and issue types. By using both services independently, you can extract standard entities from the prebuilt API and custom entities from a trained custom NER model, then combine the results. This approach avoids the limitations of a single model that cannot handle both predefined and custom entity types simultaneously.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the prebuilt entity recognition API only and map standard entities to custom categories.
Why it's wrong here
Prebuilt entities do not recognize custom entities.
- ✗
Use a single conversational language understanding (CLU) project with entities defined for both standard and custom entities.
Why it's wrong here
CLU projects define entities manually, not using prebuilt models.
- ✗
Use a custom text classification model to classify the transcript and then extract entities from the classified output.
Why it's wrong here
Text classification does not extract entities.
- ✓
Use a custom named entity recognition (NER) model for custom entities and call the prebuilt entity recognition API separately for standard entities.
Why this is correct
Combines both approaches to cover all entity types.
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