Implement knowledge mining and information extraction solutions →mediumMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your organization is implementing a knowledge mining solution for a research institute that needs to extract chemical compound names and reactions from scientific articles in PDF format. The solution must use a custom model because the scientific terminology is not covered by built-in skills. You have trained a custom model using Azure AI Language's custom entity recognition (NER) and deployed it as a REST endpoint. You are using Azure AI Search with a skillset. How should you integrate the custom NER model into the enrichment pipeline?
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
✓
Create a custom skill that calls the custom NER endpoint and map the output to the index fields.
Custom NER models must be integrated into the enrichment pipeline via a custom skill that calls the model's REST endpoint and maps the extracted entities to index fields. Built-in Entity Recognition skills cannot use custom models (so C is wrong). LUIS is designed for language understanding, not custom NER (so B is wrong). Indexers cannot directly call external APIs; they rely on skills in the skillset (so D is wrong).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a custom skill that calls the custom NER endpoint and map the output to the index fields.
Why this is correct
Custom skills allow integration with any REST API, including custom NER.
- ✗
Use a Language Understanding (LUIS) app to extract entities and call it from a custom skill.
Why it's wrong here
LUIS is for intent recognition, not NER; custom NER is the appropriate Azure AI Language feature.
- ✗
Use the built-in Entity Recognition skill and configure it with your custom model's endpoint.
Why it's wrong here
Built-in skills are fixed; they cannot be pointed to custom endpoints.
- ✗
Configure the indexer to call the custom NER endpoint directly during indexing.
Why it's wrong here
Indexers do not make direct API calls; enrichment is done through skills.
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