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Microsoft AI-103 Exam Syllabus Topics:

Section	Weight	Objectives
Implement text and speech analysis solutions	10–15%	- Implement speech capabilities 1. Speech translation and speaker recognition 2. Speech-to-text and text-to-speech integration - Implement natural language processing 1. Build conversational language understanding 2. Use Azure AI Language services 3. Perform sentiment analysis, entity recognition, and summarization
Implement information extraction and knowledge mining	10–15%	- Build knowledge bases and search solutions 1. Create and manage vector indexes 2. Implement Azure AI Search 3. Design knowledge mining pipelines - Extract structured data from documents 1. Process forms, invoices, and unstructured content 2. Use Azure AI Document Intelligence
Plan and manage Azure AI solutions	25–30%	- Design Azure AI infrastructure 1. Design for scalability, availability, and cost optimization 2. Plan for security, compliance, and responsible AI 3. Select appropriate Azure AI Foundry services - Manage AI solution development lifecycle 1. Monitor and maintain AI workloads 2. Integrate with CI/CD pipelines 3. Configure model and agent deployments
Implement generative AI and agentic solutions	30–35%	- Design and implement intelligent agents 1. Select agent architecture patterns 2. Manage state, memory, and context 3. Integrate agents with external systems and data sources 4. Implement multi-agent workflows and orchestration - Build generative AI applications 1. Build retrieval-augmented generation (RAG) solutions 2. Implement function calling and tool use 3. Implement prompt engineering and optimization 4. Integrate Azure OpenAI and other models
Implement computer vision solutions	10–15%	- Build multimodal solutions 1. Combine vision and language capabilities 2. Process and analyze video content - Implement image analysis and processing 1. Use Azure AI Vision services 2. Implement object detection and image classification 3. Extract text and structure from images

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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q69-Q74):

NEW QUESTION # 69
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. Self-hosted deployment on Azure Kubernetes Service
B. A local container running on a developer workstation
C. Serverless API deployment, also called models as a service (MaaS)
D. Managed compute (managed endpoint) deployment

Answer: C

Explanation:
Serverless API deployment runs on Microsoft-managed infrastructure, bills per input and output token, and needs no VM quota, so it is the fastest path that matches token-based billing. It is the standard choice when you want pay-per-use economics without managing compute.

NEW QUESTION # 70
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.

Answer:

Explanation:

Explanation:
Metrics to enable: Model Availability Rate and Provisioned Utilization
Diagnostic log to collect: trace
The correct metrics are Model Availability Rate and Provisioned Utilization . Model Availability Rate identifies whether failures are caused by service-side model unavailability because it is calculated from total calls minus server errors. Provisioned Utilization identifies resource-limit conditions because Microsoft states that when utilization reaches or exceeds 100%, calls are throttled and HTTP 429 errors are returned. This directly maps to the reported peak-hour rate-limit errors.
The correct diagnostic log is trace . For an internal Q & A scenario, trace logging is required to analyze question-answering behavior, including unanswered questions. Microsoft's custom question answering analytics guidance states that diagnostic logging stores telemetry and chat logs and instructs enabling Trace in addition to Audit, RequestResponse, and AllMetrics. The sample Kusto queries for unanswered questions inspect the answer, question, score, and knowledge base ID, and filter unanswered results by a score of zero.
RequestResponse is useful for request status and latency, and Audit is for administrative operations, but neither is the best diagnostic category for analyzing Q & A inference behavior such as "No relevant information found." Reference topics: Foundry monitoring, model availability, provisioned utilization, diagnostic logging, custom question answering analytics, and trace logs.

NEW QUESTION # 71
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 user prompts.
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
The solution does not meet the goal. Prompt Shields for user prompts are designed to detect direct attempts by a user to manipulate the model through the prompt itself. In this scenario, the malicious instructions are embedded inside uploaded images and then introduced into the model context through extracted image text.
That pattern is an indirect prompt injection or document attack, not merely a direct user-prompt attack.
Microsoft's Prompt Shields guidance distinguishes between user prompt attacks and document attacks, and states that document attacks involve harmful instructions embedded in supplied documents or third-party content.
The solution is also incomplete because users can upload unsafe images. Azure AI Content Safety includes image APIs that detect harmful content in images and support moderation across modalities. A complete mitigation would combine image moderation for unsafe visual content with Prompt Shields for document attacks, and optionally Spotlighting, so OCR-derived or embedded image text is treated as lower-trust context.
Prompt Shields for user prompts alone would not reliably block unsafe images or hidden instructions extracted from those images. Reference topics: Azure AI Content Safety, Prompt Shields, user prompt attacks, document attacks, image moderation, and multimodal safety.

NEW QUESTION # 72
You have an Azure subscription that contains an Azure OpenAI resource.
You plan to build an agent by using the Azure AI Agent Service. The agent will perform the following actions:
- Interpret written and spoken questions from users.
- Generate answers to the questions.
- Output the answers as speech.
You need to create the project for the agent.
What should you use?

A. Azure AI Foundry
B. the Azure portal
C. Language Studio
D. Speech Studio

Answer: A

Explanation:
Azure AI Foundry is a platform for designing, customizing, managing, and supporting AI applications and agents. It acts as an AI app factory, providing a unified environment with tools, models, and deployment pipelines for various AI tasks. It enables teams to build and operate AI solutions, including those powered by generative AI, while ensuring security, governance, and cost-efficiency.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry

NEW QUESTION # 73
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 URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.

A. Add the image URL to request metadata.
B. Place the image URL inside the system message.
C. Set the user message content array to include items with type: text and type: image_url.
D. Include base64 data directly inside the content string.

Answer: C

Explanation:
A vision-enabled Chat Completions request must represent the user message as a multimodal content array.
One item contains the prompt with " type " : " text " , while another contains the image reference with " type ": " image_url " and an image_url.url property:
" content " : [
{ " type " : " text " , " text " : " Analyze this image. " },
{ " type " : " image_url " , " image_url " : { " url " : " < image-url > " }}
]
This structure explicitly identifies each modality, allowing the deployed model to route the text and image through the appropriate processing paths. A URL embedded in an ordinary string remains text and is not interpreted as visual input. Microsoft's documented Chat Completions schema shows the text and image objects inside the user message's content array.
Request metadata does not define model input, and a system message should contain behavioral instructions rather than the user's image attachment. Base64 images are supported, but the encoded value must be formatted as a data URL and supplied through the structured image item-not inserted into a plain content string.
Study Guide alignment: deploy and consume multimodal models, integrate generative workflows into applications, and design multimodal-understanding workflows .

NEW QUESTION # 74
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