AI-103 Dumps To Pass Microsoft Exam in 24 Hours – TrainingDump [Q16-Q33]

AI-103 Dumps To Pass Microsoft Exam in 24 Hours – TrainingDump [Q16-Q33]

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AI-103 Dumps To Pass Microsoft Exam in 24 Hours – TrainingDump

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Microsoft AI-103 Exam Syllabus Topics:

Section Objectives
Implement Natural Language Processing Solutions – Language understanding and intent recognition
– Translation and multilingual support
– Text analytics and summarization
Plan and Manage Azure AI Solutions – Model selection and lifecycle management
– Azure AI resource provisioning and configuration
– Responsible AI principles and governance
Knowledge Mining and Information Retrieval – Azure AI Search configuration
– RAG (Retrieval Augmented Generation) patterns
– Indexing and semantic search
Develop Generative AI Applications and Agents – Azure OpenAI Service integration

  • 1. Function calling and tool use
    • 2. Prompt engineering and prompt flow design

      – AI agents architecture

      • 1. Memory and state management
        • 2. Agent orchestration and workflows
          Implement Computer Vision Solutions – OCR and document intelligence
          – Image classification and object detection

           

          NO.16 You have a custom agent named Agent1.
          You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
          What should you do first?

           
           
           
           

          NO.17 You have a Microsoft Foundry project named Project1 that contains an agent. The agent uses an OpenAPI 3.0 specification to call an external weather service.
          The weather service requires a key to be passed in an HTTP header. The key value is stored as a connection in Project1.
          You need to ensure that the key value from the connection is included automatically whenever the OpenAPI tool is invoked.
          What should you configure in the OpenAPI specification?

           
           
           
           

          NO.18 Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
          After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
          You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
          You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
          You need to implement controls to mitigate the risk.
          Solution: You configure image moderation to block unsafe content before processing the images.
          Does this meet the goal?

           
           

          NO.19 You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure Al Search resource.
          You need to recommend a solution to centrally manage the Azure Al Search credentials within Project1. The solution must be implemented across all the agents.
          What should you recommend?

           
           
           
           

          NO.20 You have a Microsoft Foundry project that generates product marketing images from text prompts.
          After publishing several images, the legal team at your company identifies a competitor’s logo on a sign in the background of an image.
          You need to remove only the logo, while preserving the rest of the image.
          What should you do?

           
           
           
           

          NO.21 Your company is piloting a customer support agent in a Microsoft Foundry project name Project1. Project1 is connected to an existing Application Insights resource, and the company ‘ s support team reviews runs in the Traces tab.
          The Foundry Agent Service is configured to perform the following actions:
          * Retrieve the Application Insights connection string by calling
          project_client.telemetry.get_application_insights_connection_string().
          * Call configure_azure_monitor(connection_string=…) to enable telemetry.
          A separate LangChain service configured to use OpenTelemetry and has the following configurations:
          * Uses AzureAIOpenTelemetryTracer(connection_string=…, enable_content_recording=False)
          * Passes the tracer by using config={ ” callbacks ” :[azure_tracer]}
          Company policy has the following requirements:
          * Telemetry from LangChain and OpenTelemetry must be distinguishable within the same Application Insights resource.
          * Secrets and credentials must NOT be stored in prompts, tool arguments, or span attributes.
          For each of the following statements, select Yes if the statement is true. Otherwise, select No.
          NOTE: Each correct selection is worth one point.

          NO.22 You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
          The agent generates original images from user-supplied product photos.
          You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
          What should you do?

           
           
           
           

          NO.23 You have a Microsoft Foundry project that contains an agent named Agent1. Agent1 runs successfully, but Foundry Control Plane does NOT display values for error rates, runs, and token usage, and the Traces tab is empty. You need to ensure that Foundry Control Plane displays the appropriate values for Agent1.

           
           
           
           

          NO.24 You have an Azure subscription that contains an Azure Language in Foundry Tools service resource. You need to identify the URL of the REST interface for the Language service. Which blade should you use in the Azure portal?

           
           
           
           

          NO.25 You build a chatbot that uses the Azure OpenAI GPT-4 model to generate song lyrics.
          You need to ensure that responses do NOT contain lyrics from popular songs that might have been ingested during model training.
          Which Azure AI Content Safety API should you use?

           
           
           
           

          NO.26 You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1.
          What should you include in the recommendation?

           
           
           
           

          NO.27 You have a Microsoft Foundry project that contains an internal Q & A agent.
          Users report the following issues when they ask the agent questions:
          * An increase in the following response: “No relevant information found”
          * Periodic HTTP 429 rate limit exceeded errors during peak hours
          You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
          What should you do? To answer, select the appropriate options in the answer area.
          NOTE: Each correct selection is worth one point.

          NO.28 You plan to build an agent that will combine and process multiple files uploaded by users.
          You are evaluating whether to use the Azure AI Agent Service to develop the agent.
          What is the maximum size of all the files that can be uploaded to the service?

           
           
           
           

          NO.29 You are building a chatbot.
          You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
          Which service should you use to classify the input?

           
           
           
           

          NO.30 You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
          Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
          Sometimes, mixed-language segments result in incomplete or incorrect translations.
          You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
          What should you do before sending the segments to Translator?

           
           
           
           

          NO.31 You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
          Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
          Sometimes, mixed-language segments result in incomplete or incorrect translations.
          You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
          What should you do before sending the segments to Translator?

           
           
           
           

          NO.32 You are creating an image-editing workflow in a Microsoft Foundry project.
          The workflow must meet the following requirements:
          * Ensure that background objects can be removed by applying a mask-based inpainting edit.
          * Preserve the original lighting and style of the edited images.
          * Use the built-in image editing controls, NOT a custom model.
          You need to ensure that image edits apply exclusively inside the masked area.
          How should you configure the workflow?

           
           
           
           

          NO.33 You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project.
          User messages can contain both text and images.
          Currently, App1 includes image URL: as plain text inside the message content so the model cannot recognize them as images.
          Traces show that the requests contain a single text message instead of a multimodal content array.
          You need to send the message as a structured array that includes both the text portion and the image reference to ensure that the model can process the image correctly.
          What should you do?

           
           
           
           

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