The correct answer is to create a custom skill that calls the custom NER endpoint and map the output to the index fields. This is necessary because Azure AI Search skillsets can only leverage built-in skills for standard entity recognition, but when integrating a custom NER model into an AI Search enrichment pipeline, you must wrap the model’s REST endpoint in a custom skill that the skillset can invoke during indexing. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of the skillset architecture—specifically that the indexer orchestrates document processing but delegates enrichment to skills, which are the only components allowed to call external APIs. A common trap is confusing the indexer’s role with that of a skill, or assuming built-in skills can be retrained with custom data. Remember the memory tip: “Skills call APIs, indexers call skills”—custom NER always requires a custom skill wrapper.
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. 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.
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 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.
Option B is correct because custom NER models can be called via a custom skill in the skillset. Option A is wrong because the built-in Entity Recognition skill cannot use custom models. Option C is wrong because Language Understanding is not for NER. Option D is wrong because the indexer does not directly call external APIs; skills do.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 that calls the custom NER endpoint and map the output to the index fields. — Option B is correct because custom NER models can be called via a custom skill in the skillset. Option A is wrong because the built-in Entity Recognition skill cannot use custom models. Option C is wrong because Language Understanding is not for NER. Option D is wrong because the indexer does not directly call external APIs; skills do.
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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