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Microsoft AI-103 Exam Syllabus Topics:

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

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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q89-Q94):

NEW QUESTION # 89
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.

You need to add the correct tool _choiceparameter to the code to deterministically force the agent to invoke the MCP tool on each run.
What should you add?

A. tool_choice={"auto"}
B. tool_choice={"type":"knowledge_base"}
C. tool_choice={"required"}
D. tool_choice ={"type":"mcp"}

Answer: C

Explanation:
To deterministically force the agent to invoke your Model Context Protocol (MCP) tool on every run, you must pass tool_choice="required" into the run_create_and_process method.
The 'required' tool choice: Setting this parameter to 'required' forces the underlying Azure OpenAI model to invoke one of your available tools on every response, ensuring the agent doesn't guess answers from the base model.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/tool-best-practice

NEW QUESTION # 90
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?

A. safety metrics
B. token usage
C. tracing
D. monitoring

Answer: C

Explanation:
Here is LLM tracing (also known as trace view or distributed tracing for GenAI) needed.
Tracks Execution Flow: It captures the exact ordered sequence of LLM calls and tool invocations.
Pinpoints Latency: It provides timestamps and durations for every individual step to catch the 15- second bottlenecks.
Inspects Inputs/Outputs: It lets you see the exact payload sent to and from the knowledge API and the final LLM prompt to find out why the agent hallucinated or ignored the data.
Reference:
https://coralogix.com/ai-blog/advanced-techniques-for-monitoring-traces-in-ai-workflows/

NEW QUESTION # 91
You have a Microsoft Foundry project that contains an agent. The agent uses Azure AI Search for Retrieval Augmented Generation (RAG). You plan to ingest and index PDF product manuals. You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.

A. analyzers
B. semantic ranking
C. suggesters
D. vector search

Answer: D

Explanation:
Use vector search because it performs similarity matching based on semantic meaning rather than requiring literal keyword overlap. During ingestion, the product manuals should be divided into appropriately sized chunks. An embedding model converts each chunk into a numerical vector, which Azure AI Search stores in a vector field. At query time, the user's question is also converted into an embedding, and the service retrieves chunks whose vectors are closest to the query vector. Consequently, wording such as "reset the device to factory settings" can match manual content such as "restore the unit's original configuration," despite minimal lexical overlap. Azure AI Search explicitly defines vector fields as enabling similarity searches over mathematically represented content and supports this retrieval pattern for RAG applications.
Semantic ranking improves the ordering of an existing result set but is not the indexing mechanism that stores embeddings and performs nearest-neighbor similarity matching. Suggesters provide search-as-you-type functionality, while analyzers control tokenization and lexical processing for text fields.
This aligns with the AI-103 Study Guide objectives for implementing RAG, configuring Azure AI Search grounding, generating embeddings, and selecting vector retrieval techniques for agentic solutions.

NEW QUESTION # 92
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. agent
C. output item
D. conversation

Answer: D

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 # 93
You have an invoice-processing application named App1 that uses Azure Constant Understanding in Foundry Tools.
You are building a new Content Understanding pipeline named Pipeline1 that must meet the following requirements:
- Compare an invoice to its related purchase order
- Validate the voice against static vendor contact documents
- Return a single structured output that includes discrepancy findings
You need to configure Pipeline1 and expose the pipeline as a single analyzer endpoint. What should you configure?

A. a multi-file task in standard mode that uses the invoice and purchase order as input to the analyzer
B. a multiple-file task in pro mode that uses the vendor contract files as reference data
C. a single-file task in standard mode that uses the vendor contract provided as an additional document during analysis.
D. a single-file task in standard mode that uses confidence scores enabled for the extracted fields.

Answer: B

Explanation:
Multiple-file task: Required over a single-file task. Your pipeline needs to reconcile data across two separate active transactional documents (the invoice and the purchase order) within a single analyzer request.
Pro mode is required instead of standard mode. Pro mode is specifically designed for advanced scenarios requiring multi-step reasoning, cross-file analysis, and validation against a knowledge base. Note that Pro mode currently supports classify and generate fields but does not support confidence scores for specific extracted fields.
Vendor contract files as reference data: Required. Because the vendor contracts are static compliance documents, they should be uploaded and treated as the analyzer's background knowledge base (reference data) to guide the validation logic.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/standard-pro-modes

NEW QUESTION # 94
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