Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your company is building a knowledge base for customer support using Azure AI Search. You have a large dataset of customer emails stored in Azure Blob Storage. The solution must extract key phrases, detect sentiment, and identify customer intents (e.g., complaint, inquiry, feedback). You plan to use built-in AI skills for key phrase extraction and sentiment detection. For intent identification, you need a custom solution because the intents are specific to your business. You have trained a custom Language Understanding (LUIS) model and published it. How should you integrate the LUIS model into the Azure AI Search enrichment pipeline to extract intents?
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 in the skillset that calls the LUIS endpoint and returns the top intent.
Azure AI Search allows you to create a custom skill that can call an external API, such as a LUIS endpoint, to extract intents specific to your business. This integrates seamlessly into the enrichment pipeline. Option A is incorrect because Document Intelligence is for document analysis, not intent classification. Option B is incorrect because custom analyzers are for indexing text, not enrichment. Option C is incorrect because the built-in Entity Recognition skill can identify general entities but cannot identify custom business-specific intents. Option D is correct because a custom skill can invoke the LUIS model to return the top intent, and this can be mapped to a field in the index.
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 Document Intelligence skill to classify intents.
Why it's wrong here
Document Intelligence is for form and document extraction, not intent classification.
- ✗
Configure the index to use a custom analyzer to parse intents.
Why it's wrong here
Analyzers are for text indexing and search, not enrichment.
- ✗
Use the built-in Entity Recognition skill to extract intents.
Why it's wrong here
Entity Recognition extracts named entities, not intents.
- ✓
Create a custom skill in the skillset that calls the LUIS endpoint and returns the top intent.
Why this is correct
Custom skills enable integration with external services like LUIS for custom entity or intent extraction.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.