AI-102 Practice Question: Implement natural language processing solutions
You are building a chatbot that uses Azure AI Language to extract intents and entities from user utterances. The bot must recognize custom entities like product names that are not in the default model. Which feature should you use?
⚠ Common exam trap
In Azure AI Language, a common pitfall is confusing the 'list entity' component (which relies on exact or fuzzy matching of a predefined list) with the 'custom NER' component (which uses a trained model to extract entities even when they appear in novel forms). For custom product names that may vary or are not in a fixed list, custom NER is the correct choice, not a list entity.
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
✓
Custom named entity recognition (NER) component.
Custom named entity recognition (NER) is the correct feature because it allows you to train a model to identify domain-specific entities, such as product names, that are not included in Azure AI Language's prebuilt entity catalog. Unlike prebuilt components, custom NER uses a labeled dataset to learn the exact spans of text that represent your custom entities, enabling the chatbot to extract them accurately from user utterances.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prebuilt entity recognition component.
Why it's wrong here
Only recognizes common entities like dates, numbers, etc.
- ✗
Key phrase extraction.
Why it's wrong here
Extracts key phrases, not specific entities.
- ✓
Custom named entity recognition (NER) component.
Why this is correct
Allows training a model to extract custom entities.
- ✗
List entity in a conversational language understanding (CLU) project.
Why it's wrong here
Matches exact terms but does not train a model to recognize variations.
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