AI-102 Practice Question: Implement knowledge mining and document intelligence solutions
A company uses Azure AI Search to index documents from an Azure SQL Database. They have configured an indexer with a skillset that includes a custom skill hosted in an Azure Function. The custom skill enriches each document with a 'category' field. After running the indexer, they notice that the 'category' field is missing in the index for all documents. The Azure Function logs show that it is receiving requests and returning responses. What is the most likely cause?
⚠ Common exam trap
The trap here is focusing on the skill's execution or function code, but the missing field is often due to a missing output field mapping in the indexer.
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
✓
The output field mapping for the custom skill is not correctly configured in the indexer.
The most likely cause is a missing or incorrect output field mapping. The custom skill executes and returns a response, but if the indexer is not configured to map that output to an index field, the enriched data never gets stored. This is a common configuration oversight: the skill definition includes an output, but the indexer's outputFieldMappings must explicitly associate it with a target field.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The indexer's data source connection string is invalid, so documents are not being retrieved.
Why it's wrong here
If the data source connection string were invalid, the indexer would fail to retrieve documents and likely report an error. The scenario implies documents are being processed because the custom skill is receiving requests. Therefore, the data source is working, and the issue is downstream in the enrichment or mapping process.
- ✓
The output field mapping for the custom skill is not correctly configured in the indexer.
Why this is correct
This is correct because if the custom skill's output is not mapped to an index field, the enriched data will not appear in the index. Even though the skill runs successfully, the indexer needs an outputFieldMapping to send the skill's output to the target field. Without it, the category data is discarded, resulting in missing values in the index.
- ✗
The custom skill's context is set to /document/pages/*, but the documents do not have a 'pages' node.
Why it's wrong here
If the context path does not exist in the document, the skill would not execute for any document. However, the logs show the function is receiving requests, so the skill is executing. Thus, the context is likely valid. The issue is not with context but with how the output is mapped to the index.
- ✗
The custom skill's Azure Function is returning a 200 OK status but with an empty response body.
Why it's wrong here
If the function returned an empty response body, the skill would not produce any output, but the logs indicate it is returning responses. However, the problem states that the function is returning responses, which implies non-empty. Even if it were empty, the root cause would still be a mapping issue if the output is not mapped. But the scenario suggests responses are present.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.