| Exam Code | AI-103 |
| Exam Name | Developing AI Apps and Agents on Azure |
| Questions | 67 Questions Answers With Explanation |
| Update Date | June 22,2026 |
| Price |
Was : |
Welcome to Certsleader, your ultimate source for top-quality AI-103 dumps tailored for Microsoft AI-103 exam. Our comprehensive resources are designed to help you excel in your exam preparations and achieve your certification goals. Whether you are a beginner looking to start a career in Microsoft or an experienced professional seeking to advance your skills, Certsleader has the right tools to support your journey.
At Certsleader, we are committed to your success. Our practice questions answers are designed to improve your knowledge and help you pass your exams on the first attempt with high scores. In the rare event that you do not succeed, we offer a full refund, taking responsibility for your satisfaction.
Join thousands of satisfied learners who have successfully passed their certification exams with Certsleader. Explore our study materials, download your PDF files, and take the first step towards a rewarding IT career today.
You have a customer support agent that uses the Microsoft Foundry Agent Service. Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following: • Multi-turn continuity within the session • Cross-session continuity for the same case • Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn. What should you do?
A. Persist only the final model response stored in the client application and prepend the
response to future prompts.
B. Enable memory summarization on the agent definition to persist the context automatically.
C. Create and reuse a conversation by storing the conversation’s ID and supplying the ID on subsequent requests.
You have a Microsoft Foundry project. You plan to build a customer support solution that contains an agent. The solution must meet the following requirements: • Provide accurate, context-aware responses grounded in internal product documentation stored in Azure AI Search. • Require deep, multi-step reasoning across long contexts. • Generate detailed natural language responses. Which type of model should you use to power the agent?
A. a multimodal model
B. a key phrase extraction model
C. a small language model (SLM)
D. a large language model (LLM)
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 increase the value of the temperature parameter. Does this meet the goal?
A. Yes
B. No
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment. App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context. Some uploaded images contain embedded text. You need to prevent potentially malicious instructions from being processed by the model. What should you use?
A. protected material text
B. prompt shields for user prompts
C. image moderation
D. prompt shields for documents
You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition. You are approaching the expiration date of the custom speech to text model. What is the expected behavior when the model expires?
A. Speech recognition requests will fall back to the most recent base model for the same locale.
B. Speech recognition requests will continue to use the expired custom model until the model is removed manually.
C. Speech recognition requests will return a 4xx error until a new custom model is deployed.
D. The custom model will be deleted automatically when the model expires.
You have a Microsoft Foundry project that contains a model deployment. You have an application that calls the deployment by using the Azure OpenAl v1 API and DefaultAzureCredential. The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login. You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege. Which role-based access control (RBAC) role should you assign to the developers?
A. Cognitive Services OpenAl User
B. Cognitive Services Data Reader
C. Cognitive Services User
D. Contributor
You have a Microsoft Foundry project that uses Azure Al Search to ground an agent in internal documentation. After a recent content update, users report that the agent's answers have become less accurate. You need to identify whether the retrieved content is negatively influencing the model's generated responses. Which observability signal should you review?
A. prediction drift metrics
B. groundedness evaluation metrics
C. latency breakdown traces
D. indexer status and failure history
You have a Microsoft Foundry project that contains an agent. The agent ingests scanned PDF vendor invoices that contain tables and embedded QR codes. The agent must preserve the PDF layout in the extracted output to ensure that downstream processing can reference sections and tables. You plan to call Azure Content Understanding in Foundry Tools. You need to extract content and layout elements and detect QR codes without requiring a language model deployment. Which built-in analyzer should you use?
A. prebuilt-layout
B. prebuilt-documentFieldSchema
C. prebuilt-read
D. prebuilt-documentSearch
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed line items and has a table-based layout. Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution. You need to create a single analyzer that meets the following requirements: • Extracts the invoice number, invoice date, vendor name, and total amount across varying templates • Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review What should you use?
A. the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
B. a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
C. a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
D. the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Al Search as the retriever. You plan to ingest PDFs into an Azure Al Search index to ensure that the agent can ground responses in texts in both documents and embedded images. Users require citations that link to the source files. You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill. Which indexing approach should you use?
A. a skillset to run the OCR skill directly against the content field of the index
B. the outputFieldMappings parameter to write image data to a searchable field
C. an indexer to extract image data into a normalized_images collection
D. a Shaper skill to restructure the OCR input
You have a Microsoft Foundry project that contains an agent. The agent has a Model Context Protocol (MCP) tool that queries a knowledge base stored in Azure AI Search. Some agent runs return answers from the base model without invoking the knowledge base, which results in responses without grounded citations. You are provided with the following code snippet that runs the agent. run = project_client.agents.runs.create_and_process( thread_id=thread.id, agent_id=agent.id, ) You need to add the correct tool_choice parameter to the code to deterministically force the agent to invoke the MCP tool on each run. What should you add?
A. tool_choice ={"type":"mcp"}
B. tool_choice={"auto"}
C. tool_choice={"type":"knowledge_base"}
D. tool_choice={"required"}
You have a Microsoft Foundry project that contains an agent. The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables. You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata. Users frequently ask questions that require the retrieval of specific table rows across the pages. You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk. How should you configure the ingestion job?
A. Use basic parsing and fixed-size chunking.
B. Use advanced data parsing to reingest the documents.
C. Use OCR and page-level chunking.
D. Use page-level OCR extraction and store each page as a single chunk.
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 increase the value of the max_tokens parameter. Does this meet the goal?
A. Yes
B. No
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?
A. Yes
B. No
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?
A. Use an embeddings model to encode the audio, and then decode the audio into text and
speech.
B. Use batch transcription to convert the audio input and return text responses from the agent.
C. Use speech translation to convert the audio into another language and return the translated text.
D. Use real-time speech to text for incoming audio and text to speech for agent responses.
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?
A. the custom speech endpoint URL
B. the project URL
C. the project ID
D. the custom speech project ID
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. Enable a managed identity for each application and call Azure AI Search directly.
B. Create a custom HTTP connection in Foundry and manually configure Azure AI Search endpoints per application.
C. Call Azure AI Search directly from each application by using Microsoft Entra authentication.
D. Configure an Azure AI Search connection in Project1 and reference the connection in each application.
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?
A. an Azure Key Vault connection
B. a header parameter defined for each operation
C. an API key security scheme
D. a Bearer token security scheme
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?
A. Specify English as the source language in the translation request for all the segments.
B. Enable automatic language detection for the translation request.
C. Split the mixed-language segments into single-language segments and translate each segment separately.
D. Use document translation to translate the entire transcript as a single document.
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?
A. Increase the value of the max_tokens parameter for all the requests.
B. Route all the requests to a smaller model.
C. Route all the requests to the most capable model.
D. Use a model cascade that routes the requests to different models.
