The correct answer is to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. This is necessary because Azure Cognitive Search’s built-in AI skills only handle prebuilt entities like persons or locations, not domain-specific entities from a custom model deployed in Azure AI Language. By defining a custom web API skill in your skillset, you configure the enrichment pipeline to send document text to your custom endpoint and receive back the extracted chemical compounds and diseases, which can then be mapped to fields in the search index. On the AI-102 exam, this scenario tests your understanding of how to extend a skillset beyond out-of-the-box capabilities, and a common trap is assuming a built-in skill can be retrained or that the custom model can be referenced directly without a custom skill wrapper. Remember the key principle: if the model is not a prebuilt Cognitive Services resource, you must wrap it in a custom skill. A helpful memory tip is “Custom model, custom skill”—you cannot call a custom endpoint without a custom skill acting as the bridge.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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?
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.
Option B is correct because you need to create a custom skill in the Azure Cognitive Search skillset that calls the Azure AI Language custom entity extraction endpoint. The built-in skills do not support custom models directly. Option A is wrong because built-in skills only cover prebuilt entities. Option C is wrong because the custom model is already deployed. Option D is wrong because a custom skill is needed before adding to the index.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Static NAT maps one inside address to one outside address..
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. — Option B is correct because you need to create a custom skill in the Azure Cognitive Search skillset that calls the Azure AI Language custom entity extraction endpoint. The built-in skills do not support custom models directly. Option A is wrong because built-in skills only cover prebuilt entities. Option C is wrong because the custom model is already deployed. Option D is wrong because a custom skill is needed before adding to the index.
What should I do if I get this AI-102 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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