The correct choice is to create a custom skill in the skillset that calls the LUIS endpoint and returns the top intent. This works because Azure AI Search’s enrichment pipeline allows you to define custom web API skills, which can invoke any external service—including your published LUIS model—to perform specialized processing like intent identification. Built-in skills handle key phrase extraction and sentiment, but for business-specific intents like “complaint” or “inquiry,” you must extend the pipeline with a custom skill that sends text to LUIS and maps the response into the index. On the AI-102 exam, this scenario tests your understanding of how to augment the default cognitive skills with custom logic, a common trap being to assume LUIS is a built-in skill or that you can modify the index directly. Remember the memory tip: “Custom skill for custom intent”—if Azure doesn’t offer it out of the box, you build a custom skill to call the external API.
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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 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.
Option A is correct because Azure AI Search supports custom skills that can call external APIs, such as a LUIS endpoint. This allows you to integrate the LUIS model for intent extraction. Option B is wrong because LUIS is not a built-in skill. Option C is wrong because modifying the index does not run enrichment. Option D is wrong because Document Intelligence is not for intent extraction.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a custom skill in the skillset that calls the LUIS endpoint and returns the top intent. — Option A is correct because Azure AI Search supports custom skills that can call external APIs, such as a LUIS endpoint. This allows you to integrate the LUIS model for intent extraction. Option B is wrong because LUIS is not a built-in skill. Option C is wrong because modifying the index does not run enrichment. Option D is wrong because Document Intelligence is not for intent extraction.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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