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| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Topic 2: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 3: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Topic 4: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 5: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
>> AI-103 Examcollection Questions Answers <<
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NEW QUESTION # 129
You are designing a solution that will answer questions about human resources (HR) policies stored in the PDF format.
You need to ensure that the identical answer to a specific question is returned every time. The solution must minimize development effort.
Which service should you include in the solution?
Answer: A
Explanation:
You can create a Custom FAQs PDF Solution Powered by Azure OpenAI with Citations from Grounded Data.
Business Problem
Imagine an organization that frequently deals with FAQs, legal documents, or reports. They want an automated system that can:
Generate answers from questions using the information found in existing PDF documents.
Ensure citations from those documents are included in the responses.
Output a structured PDF with the Q&A and citations formatted in a professional and readable way.
Solution Overview
By leveraging Azure OpenAI on your data and Azure Cognitive Search, we can ground the OpenAI GPT model with custom documents like PDFs. When queried, the model retrieves the most relevant responses from the documents and includes citations such as document titles or page numbers and create a question-and-answer format JSON file. The system then generates a structured, professional-looking PDF with bold questions, clear answers, and citations.
Reference:
https://www.linkedin.com/pulse/creating-custom-faqs-pdf-solution-powered-azure-openai-saqlain-tahir-x9gse
NEW QUESTION # 130
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure Al Content Safety must access the images by using the blob URL. The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Guardrails: Select User input, Output, Tool response, and Tool call and set Action to Block.
Storage access: A system-assigned managed identity that is assigned the Storage Blob Data Contributor role The guardrail must be applied to User input, Output, Tool response, and Tool call with the action set to Block . Microsoft Foundry guardrails support four intervention points: user input, tool call, tool response, and output. This scenario includes user-provided screenshots, a ticketing tool that uploads images and returns blob URLs, and final agent responses. Applying blocking controls at all four points ensures harmful image-related content is inspected throughout the agent run and prevented from continuing or being returned to the user.
Microsoft's guardrails guidance also states that tool call and tool response controls are specifically required when harmful content can pass through agent tools.
For storage, configure the Azure AI Content Safety resource with a system-assigned managed identity and grant it Storage Blob Data Contributor on the storage account or container. The Content Safety image moderation quickstart states that images can be supplied by blob storage URL and that the Content Safety resource must be given storage access by enabling its system-assigned managed identity and assigning Storage Blob Data Contributor or Owner; Contributor is the least-privileged valid option shown. Reference topics: Foundry guardrails, agent intervention points, image moderation, managed identity, and Azure Storage RBAC.
NEW QUESTION # 131
You need to monitor a user ' s video stream and verify that the user is alone. The solution must minimize development effort.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Spatial Analysis is the appropriate capability because the requirement is to analyze a video stream and determine how many people are present in a defined physical space.
Spatial Analysis provides pretrained person-detection and spatial-event functionality that can monitor occupancy, presence, and movement without requiring the developer to build a custom person-detection model. The application can use those detections to determine whether more than one person appears within the monitored area and generate an appropriate violation event.
Custom Vision object detection could theoretically be trained to detect people, but doing so would require data collection, labeling, model training, deployment, and additional application logic. That conflicts with the requirement to minimize development effort.
Speech-to-text analyzes microphone audio and cannot determine how many people are visually present. Azure AI Video Indexer derives insights from video but is not the specialized real-time physical-space monitoring solution represented by this scenario.
The AI-103 Study Guide includes implementing solutions that identify objects, components, or regions within images or video and building video understanding workflows, making Spatial Analysis the appropriate pretrained service choice. Microsoft Learn
NEW QUESTION # 132
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use Microsoft Foundry and the Azure CLI . Microsoft Foundry provides the model discovery and deployment experience for supported Azure-hosted generative models, including image-generation models.
From Foundry, developers can select an available image-generation model, configure a deployment name and supported model version, and expose the model through an Azure endpoint. The Azure CLI can be used to automate the underlying resource and deployment configuration, making it appropriate for repeatable infrastructure and CI/CD workflows.
Azure Machine Learning Studio is not the primary deployment surface for Azure OpenAI DALL-E deployments. Microsoft Graph provides APIs for Microsoft 365 and identity-related workloads rather than deploying generative AI image models.
DALL-E is a text-to-image model, which means the eventual application submits a natural-language image- generation prompt and receives generated image content rather than performing classification or analysis of existing images.
The AI-103 Study Guide explicitly includes design and implementation of image-generation solutions , including generating images from prompts and reference media and selecting appropriate generation controls.
Microsoft Learn
NEW QUESTION # 133
You have an Azure Al Search resource named Search1 that is used by multiple apps hosted in Azure. You need to secure Search1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to query specific indexes.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.
Answer:
Explanation:
Explanation:
* To prevent access from the internet: Create a private endpoint
* To limit access to query specific indexes: Use Azure roles
Create an Azure Private Link private endpoint for Search1 and disable public network access. The private endpoint assigns Search1 a private IP address within an Azure virtual network, allowing the hosted applications to reach the search service over the Microsoft backbone instead of through its public endpoint.
Disabling public network access ensures that requests originating from the public internet are rejected. An IP firewall would still expose the public endpoint to approved public IP addresses and therefore would not provide complete internet isolation.
For index-level authorization, enable Microsoft Entra ID role-based access control and assign Azure roles to each application's managed identity or service principal. The Search Index Data Reader role permits query and retrieval operations without allowing index modification. Azure AI Search supports scoping Search Index Data Reader or Search Index Data Contributor permissions to an individual index; custom role definitions can also be used when more precise permissions are required.
Query-key authentication is unsuitable because API keys normally provide service-level access and cannot reliably isolate applications to designated indexes.
Study Guide alignment: secure Azure AI services by configuring private endpoints, network isolation, managed identities, Microsoft Entra authentication, and role-based access control.
NEW QUESTION # 134
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