Question 508 of 993

Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped

AI-102 Practice Question: Implement knowledge mining and information extraction solutions

Q: What is the correct answer to this Implement knowledge mining and information extraction solutions AI-102 question?

The correct answer is: Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. — To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model. Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

Q: Why do candidates get Implement knowledge mining and information extraction solutions questions wrong on the AI-102 exam?

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.

This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

A
Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.
Custom skills can call external APIs, including custom model endpoints.
B
Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.
Why wrong: Document Intelligence is for form extraction, not custom entity recognition.
C
Add the custom entity extraction as a field mapping in the indexer.
Why wrong: Field mappings only map fields, they do not perform entity extraction.
D
Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.
Why wrong: Built-in skills cannot be configured to use external custom model endpoints.

Full breakdown with real-world context →

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 custom entity extraction endpoint via HTTP.

To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model. Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

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.

✓
Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.
Why this is correct
Custom skills can call external APIs, including custom model endpoints.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.
Why it's wrong here
Document Intelligence is for form extraction, not custom entity recognition.
✗
Add the custom entity extraction as a field mapping in the indexer.
Why it's wrong here
Field mappings only map fields, they do not perform entity extraction.
✗
Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.
Why it's wrong here
Built-in skills cannot be configured to use external custom model endpoints.

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.

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

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.

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FAQ

Questions learners often ask

What does this AI-102 question test?

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 custom entity extraction endpoint via HTTP. — To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model. Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

What should I do if I get this AI-102 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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