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HomeCertificationsAI-102DomainsImplement agentic AI solutions
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Implement agentic AI solutions

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AI-102 Domains

Implement an agentic solutionImplement computer vision solutionsImplement knowledge mining and information extraction solutionsImplement image and video processing solutionsImplement natural language processing solutionsImplement generative AI solutionsImplement agentic AI solutionsImplement knowledge mining and document intelligence solutionsPlan and manage an Azure AI solutionImplement content moderation solutions

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All AI-102 Implement agentic AI solutions questions (15)

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1

A company is building an agent that uses Azure OpenAI Service to answer customer queries by querying a SQL database. The agent must be able to handle complex multi-turn conversations and maintain context. Which approach should the team use to implement the agent?

2

A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?

3

A company is using Azure OpenAI Service to power a customer support agent. The agent sometimes generates incorrect information when it cannot find an answer in the knowledge base. The team wants to ensure the agent only responds using information from the knowledge base and explicitly states when it does not know the answer. Which configuration should the team use?

4

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

5

A company is developing an agent that uses Azure AI Language to extract entities and intents from user queries. The agent receives a query: 'Book a flight to Paris on Friday.' The agent should extract the intent as 'BookFlight' and entities as 'Paris' (destination) and 'Friday' (date). The team uses a custom entity extraction model. After testing, the model extracts 'Paris' as location but fails to extract 'Friday' as date. What should the team do to fix this?

6

A financial services company is building an agent that uses Azure OpenAI to generate investment advice. The agent must be monitored for toxicity and bias. Which combination of services should the team use to implement content safety monitoring?

7

A company is building an agent that needs to perform tasks like sending emails and updating a CRM system. The agent uses Azure OpenAI with function calling. The team defines functions for these tasks. When the agent is tested, it sometimes calls the wrong function or invents function names. What should the team do to improve the reliability of function calling?

8

A company is developing an agent that uses Azure AI Vision to analyze images uploaded by users. The agent must identify objects and read text in images. The team uses the Azure AI Vision API. During testing, the agent fails to read text from images with low contrast. What should the team do to improve optical character recognition (OCR) accuracy for such images?

9

A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?

10

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

11

A company wants to deploy an agent using Azure Bot Service that integrates with Microsoft Teams. Which THREE steps should the team take?

12

An agent uses Azure AI Language to perform sentiment analysis on customer feedback. The team notices that the sentiment scores are sometimes inaccurate for negative feedback. Which TWO improvements should the team consider?

13

You are building an agent for a legal firm that uses Azure OpenAI to analyze contracts. The agent must extract key clauses, identify risks, and summarize the contract. The agent uses a RAG pattern with Azure Cognitive Search as the vector database. After deployment, the agent sometimes returns irrelevant information or fails to find relevant clauses. You suspect the issue is with the chunking strategy. The contracts are large, typically 50-100 pages. Currently, you are chunking by page (each page is one chunk). You want to improve retrieval accuracy. Which action should you take?

14

Drag and drop the steps to implement an Azure AI Bot Service with QnA Maker into the correct order.

15

Match each Azure AI pricing tier to its description.

Other AI-102 exam domains

Implement an agentic solutionImplement computer vision solutionsImplement knowledge mining and information extraction solutionsImplement image and video processing solutionsImplement natural language processing solutionsImplement generative AI solutionsImplement knowledge mining and document intelligence solutionsPlan and manage an Azure AI solutionImplement content moderation solutions

Frequently asked questions

What does the Implement agentic AI solutions domain cover on the AI-102 exam?

The Implement agentic AI solutions domain covers the key concepts tested in this area of the AI-102 exam blueprint published by Microsoft. Courseiva provides free domain-focused practice, mock exams, missed-question review, and readiness tracking across all AI-102 domains — no account required.

How many Implement agentic AI solutions questions are in the AI-102 question bank?

The Courseiva AI-102 question bank contains 15 questions in the Implement agentic AI solutions domain. Click any question to see the full explanation and answer breakdown.

What is the best way to practice Implement agentic AI solutions for AI-102?

Start with a 10-question focused session to identify your baseline accuracy in this domain. Read every explanation — even for questions you answer correctly — to understand the reasoning. Once you score consistently above 80%, move to a 20–30 question session to confirm depth before moving to the next domain.

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