Microsoft AI-103 Exam Dumps

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?

You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions. The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements: . Support turn-taking dynamics, where the agent begins to generate the speech output before the user finishes speaking. . Operate with low latency to maintain a conversational experience. You need to enable both speech to text and text to speech in a real-time agent interaction. What should you do?

You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools. You fine-tune a baseline speech to text model for the en-us locale and publish the model. The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid. You need to set the project property to the correct ID. To what should you set the project property? 

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? 

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?

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?

You have a Microsoft Foundry project that serves a high-volume chat app. Most requests are simple FAQs, but some require advanced reasoning. You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions. What should you do?

You have a Microsoft Foundry project that contains an agent. The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables. You use Azure Content Understanding in Foundry Tools to process the PDFs. You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation. Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review. You need to ensure that the Content Understanding document analyzer output includes a per-field confidence score and source grounding locations within the source document. What should you do?

You are building a speech processing solution in Microsoft Foundry for a customer support platform. The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system. You need to ensure that the call transcripts appear within only a few seconds of the audio stream. What should you do? 

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?

You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAl model deployment. During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error. You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits. What should you do?

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?

You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index. You need to connect to the index to meet the following requirements: • Complex questions must retrieve information from multiple chunks. • Multi-turn conversations must influence retrieval planning. • Retrievals must run in parallel to reduce latency. Which retrieval approach should you use? 

You have a Microsoft Foundry project that contains a high-traffic agent. After a recent update, operational costs increase significantly. Monitoring confirms that the volume of user traffic to the agent remains unchanged. You suspect that changes to the request or response characteristics are causing the increase. You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage. Which observability capability should you use?

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 templategeneralizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort. What should you include in the solution?

You have a Microsoft Foundry project named Project1. Project1 contains an application that processes PDF vendor invoices. You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort. What should you do?

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 Al 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 protected material detection. Does this meet the goal? 

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?

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 Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents. Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content. You need to improve response completeness. Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold. Does this meet the goal? 

You have a Microsoft Foundry project that contains three agents as shown in the following table. Name Description TriageAgent Classifies incoming customer requests PolicyAgent Answers policy questions by searching internal content ActionAgent Creates or updates tickets by calling an HTTP API You need to orchestrate the agents to ensure that the customer requests meet the following requirements: • Support a deterministic, step-based process that uses conditional branching and shared state across the agents. • Optionally trigger a ticket action based on the triage result. The solution must minimize development effort. What should you include in the solution?  

You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses. Users report the following issues: • Some requests take more than 15 seconds to complete. • Some responses are incorrect, even when the knowledge API returns the expected data. You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information. Which observability capability should you use?

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 Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents. Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content. You need to improve response completeness. Solution: You add a reflection pass that regenerates the response if the required clauses are missing. Does this meet the goal? 

You have a Microsoft Foundry project that contains an agent. You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning. What should you configure first? 

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 Al 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 a prompt shield for documents. Does this meet the goal? 

You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app. The agent is invoked from a Python service that does NOT run in the Foundry portal. You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs. Which two components can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.