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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers. The results are often incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?

A) Azure Language in Foundry Tools
B) an Azure Machine Learning model
C) Azure Content Understanding in Foundry Tools

2. Hotspot Question
You have a Microsoft Foundry project.
- You need to create a customer support agent that meets the following
requirements:
- Grounds responses only in company policy documents stored in curated
repositories
- Retains customer preferences across separate chat sessions
How should you configure the agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. A legal team must extract clauses and inferred fields from unstructured contracts. One required field is the contract end date, which is not stated explicitly and must be derived from the start date plus the term. The team has no labelled training data. Which tool best fits?

A) An Azure AI Document Intelligence custom model trained on labelled samples
B) The Azure AI Search semantic ranker
C) Azure Content Understanding using a prebuilt-contract or zero-shot custom analyser
D) Azure Translator in Foundry Tools

4. A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?

A) Serverless API deployment, also called models as a service (MaaS)
B) Managed compute (managed endpoint) deployment
C) A local container running on a developer workstation
D) Self-hosted deployment on Azure Kubernetes Service

5. You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?

A) Create a custom HTTP connection in Foundry and manually configure Azure AI Search endpoints per application.
B) Call Azure AI Search directly from each application by using Microsoft Entra authentication.
C) Configure an Azure AI Search connection in Project1 and reference the connection in each application.
D) Enable a managed identity for each application and call Azure AI Search directly.

Solutions: