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Microsoft AI-103 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement information extraction and knowledge mining	10–15%	- Extract structured data from documents 1. Process forms, invoices, and unstructured content 2. Use Azure AI Document Intelligence - Build knowledge bases and search solutions 1. Design knowledge mining pipelines 2. Create and manage vector indexes 3. Implement Azure AI Search
Topic 2: Implement generative AI and agentic solutions	30–35%	- Design and implement intelligent agents 1. Manage state, memory, and context 2. Integrate agents with external systems and data sources 3. Implement multi-agent workflows and orchestration 4. Select agent architecture patterns - Build generative AI applications 1. Integrate Azure OpenAI and other models 2. Implement prompt engineering and optimization 3. Implement function calling and tool use 4. Build retrieval-augmented generation (RAG) solutions
Topic 3: Plan and manage Azure AI solutions	25–30%	- Design Azure AI infrastructure 1. Select appropriate Azure AI Foundry services 2. Plan for security, compliance, and responsible AI 3. Design for scalability, availability, and cost optimization - Manage AI solution development lifecycle 1. Integrate with CI/CD pipelines 2. Configure model and agent deployments 3. Monitor and maintain AI workloads
Topic 4: Implement text and speech analysis solutions	10–15%	- Implement speech capabilities 1. Speech-to-text and text-to-speech integration 2. Speech translation and speaker recognition - Implement natural language processing 1. Use Azure AI Language services 2. Perform sentiment analysis, entity recognition, and summarization 3. Build conversational language understanding
Topic 5: 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. Extract text and structure from images 2. Implement object detection and image classification 3. Use Azure AI Vision services

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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q71-Q76):

NEW QUESTION # 71
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?

A. an Azure OpenAI Responses API call that uses a multimodal model
B. a generative chat completion request
C. an Azure Language in Foundry Tools text analysis model deployment
D. an Azure Content Understanding in Foundry Tools analyzer

Answer: D

NEW QUESTION # 72
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?

A. Azure AI Language
B. Azure Machine Learning
C. Azure AI Document Intelligence
D. Azure OpenAI

Answer: D

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 # 73
You have a Microsoft Foundry project that contains a support-ticket triage agent built by using the Foundry Agent Service.
The agent uses tool to classify the ticket type and sot the ticket priority.
Sometimes, the same support case continues across multiple sessions over several days.
You need to persist state by using a durable ID to ensure that the agent can automatically reuse the full interaction history. The solution must preserve previous user messages, tool calls and tool outputs across turns and sessions.
Which runtime component should you use?

A. response
B. conversation
C. output item
D. agent

Answer: B

Explanation:
To achieve state persistence and ensure that the agent automatically reuses the full multi-session interaction history (including previous user messages, tool calls, and tool outputs), you must include a conversation component.
References:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/runtime-components

NEW QUESTION # 74
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. AI quality (AI assisted) metrics
B. similarity evaluators
C. AI quality (NLP) metrics
D. fluency evaluator
E. risk and safety metrics

Answer: A,E

Explanation:
The two evaluation categories that would be of use are AI quality (AI assisted) metrics and risk and safety metrics Risk and safety metrics: Scoring harmful content categories (such as violence, sexual content, hate speech, and self-harm) is handled directly by the specialized safety evaluators contained within this category.
AI quality (AI assisted) metrics: In Microsoft Foundry, evaluating RAG-specific criteria like groundedness and relevance requires large language models (LLMs) acting as a judge to assess semantic context. These are explicitly categorized under AI-assisted quality metrics rather than standard natural language processing (NLP) rules.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/evaluation-evaluators/rag-evaluators
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/evaluate-sdk

NEW QUESTION # 75
You have a Microsoft Foundry project that contains an agent. The agent uses two tools to perform the following actions:
- Use Azure AI Search to retrieve answers from a private product
documentation index.
- Use the web search tool to retrieve public information on the
internet.
You need to ensure that for a specific run, the agent deterministically retrieves information only from the internet.
To what should you set tool_choice?

A. {"type": "bing_grouding"}
B. {"type": "azure_ai-search"}
C. "required"
D. "auto"

Answer: A

Explanation:
To force a specific run to exclusively use public information from the internet instead of your private documents, override the tool orchestration payload at the run level. Set the tool_choice directive specifically to the web grounding tool (e.g., bing_grounding or web_search) and constrain the tools array for that run.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search

NEW QUESTION # 76
......

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