A company uses Azure AI Search with a skillset that enriches documents. They want to store the enriched data in both Azure Table storage and Azure Blob storage. They have already created a data source and an index. What should they do?

Add a knowledge store to the skillset with both table and object projections.

Use two indexers, each writing to a different storage type.

Store the enriched data in the index and export it manually.

Create two separate skillsets, one for each storage type.

A team is designing a skillset that includes multiple built-in skills and one custom skill. The custom skill is expected to be slow. They want to ensure that the indexer does not time out and that the enriched data is still stored. What should they do?

Increase the indexer execution timeout and configure a knowledge store to persist enriched data.

Remove the custom skill and use only built-in skills.

Run the indexer in offline mode to avoid timeouts.

Use a separate indexer for each skill to parallelize processing.

A company stores product manuals as PDFs in Azure Blob Storage. They want to make the text searchable in Azure AI Search. You create an Azure AI Search service and need to ingest the PDFs. What should you create first?

An index with a field for PDF binary content

A data source that points to the Blob Storage container

An indexer that connects directly to Blob Storage

A skillset that extracts text from PDFs

What is the purpose of a knowledge store in Azure AI Search?

To cache search queries for faster responses

To store the original source documents for backup

To store enriched data from the indexing pipeline for downstream analysis

To store index definitions for reuse

Microsoft Certified:Azure AI Engineer Associate

Domain 6Objective 1

Implement an Azure AI Search Solution AI-102 Practice Questions (Page 10)

Part of the Implement knowledge mining and information extraction solutions domain, which accounts for 15-20% of the AI-102 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~6–13 in this domain), expect 2–4 from this objective — we provide 55 practice questions to prepare you well beyond it. (estimate)

55questions here

11free pages

12concepts

15-20%of the exam

Questions 46–50

46expert · hard
A company uses Azure AI Search with a skillset that enriches documents. They want to store the enriched data in both Azure Table storage and Azure Blob storage. They have already created a data source and an index. What should they do?
Select an answer first
47expert · hard
A team is designing a skillset that includes multiple built-in skills and one custom skill. The custom skill is expected to be slow. They want to ensure that the indexer does not time out and that the enriched data is still stored. What should they do?
Select an answer first
48application · medium
A company stores product manuals as PDFs in Azure Blob Storage. They want to make the text searchable in Azure AI Search. You create an Azure AI Search service and need to ingest the PDFs. What should you create first?
Select an answer first
49foundation · easy
Which Azure AI Search component is used to apply AI enrichment, such as entity recognition or OCR, to documents during indexing?
Select an answer first
50foundation · easy
What is the purpose of a knowledge store in Azure AI Search?
Select an answer first

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