What is the purpose of the $orderby parameter in an Azure AI Search query?

To sort the search results by specified fields

To select which fields to return

To filter results based on a condition

You are designing a skillset for a medical records indexing solution. The pipeline must extract text from scanned documents, identify patient names and dates, and store the enriched data in a knowledge store. You are concerned about the accuracy of entity extraction. What should you do?

Use the OCR skill followed by the entity recognition skill, and configure the knowledge store to store both the extracted text and the entities

Use the key phrase extraction skill instead of entity recognition

Use a custom skill for OCR and skip entity recognition

Use only the entity recognition skill without OCR

A company wants to implement hybrid search (keyword + vector) for a product catalog. They already have an index with keyword search. They plan to add vector fields. The team is concerned about the cost of generating embeddings for millions of products. What is the most cost-effective approach to generate embeddings?

Skip embeddings and use only keyword search

Generate embeddings for all products in a separate batch process and upload them via the SDK

Use a custom skill that calls an Azure OpenAI embedding model, and run the indexer once to generate embeddings for all products

Use the built-in vectorizer in Azure AI Search

How can you extend the capabilities of an Azure AI Search skillset beyond the built-in skills?

By creating a custom skill that calls an external web API

By adding a new data source to the indexer

By modifying the built-in skills directly in the Azure portal

By using a different index schema

Your team is building a search solution for a small internal document repository. You need to provision Azure AI Search and minimize initial cost while still allowing future scaling. Which tier should you choose?

Storage Optimized L2 tier for fast indexing

Basic tier for production from the start

Standard S3 tier immediately to handle any future load

Free tier for development, then move to a Standard tier when production load requires

Microsoft Certified:Azure AI Engineer Associate

Domain 6Objective 1

Implement an Azure AI Search Solution AI-102 Practice Questions (Page 11)

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 51–55

51foundation · easy
What is the purpose of the $orderby parameter in an Azure AI Search query?
Select an answer first
52expert · hard
You are designing a skillset for a medical records indexing solution. The pipeline must extract text from scanned documents, identify patient names and dates, and store the enriched data in a knowledge store. You are concerned about the accuracy of entity extraction. What should you do?
Select an answer first
53expert · hard
A company wants to implement hybrid search (keyword + vector) for a product catalog. They already have an index with keyword search. They plan to add vector fields. The team is concerned about the cost of generating embeddings for millions of products. What is the most cost-effective approach to generate embeddings?
Select an answer first
54foundation · easy
How can you extend the capabilities of an Azure AI Search skillset beyond the built-in skills?
Select an answer first
55application · medium
Your team is building a search solution for a small internal document repository. You need to provision Azure AI Search and minimize initial cost while still allowing future scaling. Which tier should you choose?
Select an answer first

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