100% Pass Quiz 2026 Microsoft AI-103: Reliable Trustworthy Developing AI Apps and Agents on Azure Source

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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Plan and manage Azure AI solutions25–30%- 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 2: Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Manage state, memory, and context
  • 2. Select agent architecture patterns
  • 3. Implement multi-agent workflows and orchestration
  • 4. Integrate agents with external systems and data sources
- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Integrate Azure OpenAI and other models
  • 3. Build retrieval-augmented generation (RAG) solutions
  • 4. Implement function calling and tool use
Topic 3: Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
- Implement image analysis and processing
  • 1. Use Azure AI Vision services
  • 2. Extract text and structure from images
  • 3. Implement object detection and image classification
Topic 4: Implement information extraction and knowledge mining10–15%- 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 5: Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Perform sentiment analysis, entity recognition, and summarization
  • 2. Use Azure AI Language services
  • 3. Build conversational language understanding
- Implement speech capabilities
  • 1. Speech-to-text and text-to-speech integration
  • 2. Speech translation and speaker recognition

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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q85-Q90):

NEW QUESTION # 85
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?

Answer: D

Explanation:
The correct answer is a workflow . Microsoft Foundry workflows are designed to orchestrate agents and business logic as declarative, predefined sequences of actions. The official workflow guidance states that workflows are ideal when you need to orchestrate multiple agents in a repeatable process, add branching logic such as if/else, and handle variables without writing application orchestration code. This directly matches the requirement for a deterministic, step-based process with conditional branching and shared state.
In this scenario, TriageAgent can classify the request first, the workflow can store the triage result, and conditional logic can determine whether to invoke PolicyAgent, ActionAgent, or both. The ticket action is optional, so it should be triggered through a workflow condition based on the triage output. This minimizes development effort because the branching, sequencing, and variable handling are managed in the Foundry workflow rather than being manually implemented across separate runs in application code.
A group chat session is better for dynamic agent handoff, not a strict deterministic process. Threads and runs or separate app-coordinated calls require more custom orchestration. Reference topics: Microsoft Foundry workflows, multi-agent orchestration, conditional branching, variable handling, and agent-driven workflows.


NEW QUESTION # 86
Hotspot Question
You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements:
- Retain user preferences across multiple conversations.
- Enable users to provide contextual grounding by directly uploading
documents during a chat.
Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 87
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: A,C

Explanation:
Semantic Kernel provides support for the following template formats:
semantic-kernel - Built-in Semantic Kernel format.
handlebars - Handlebars template format.
liquid - Liquid template format
The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
Reference:
https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax


NEW QUESTION # 88
Drag and Drop Question
You have a Microsoft Foundry project that contains a customer support agent grounded in internal documentation.
After a recent update, users report the following issues:
- Some answers are unsupported by retrieved documents.
- A small number of responses are flagged for policy violations.
You need to evaluate each issue.
Which observability signals should you use for each issue? To answer, drag the appropriate observability signals to the correct issues. Each observability signal may be used once, more than once, or not at all. You may need to drag the spit bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 89
You have an invoice-processing application named App1 that uses Azure Content 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 invoice against static vendor contract 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?

Answer: C

Explanation:
Configure a multi-file task in pro mode . At analysis time, provide the invoice and its related purchase order as the input documents. During analyzer creation, add the static vendor contracts as reference data . Pro mode can reason across multiple input documents and use reference documents as contextual knowledge for validation, enrichment, and discrepancy detection. Microsoft specifically documents the scenario of supplying an invoice and purchase order as inputs while using contract files as reference data to identify inconsistencies.
Define a field schema containing the required invoice information and generated discrepancy findings.
Building the task creates an analyzer ID that applications invoke through one analyzer API endpoint, producing a unified structured result governed by that schema.
Standard mode is optimized for straightforward processing of individual files. It does not support cross-file analysis, reference-dataset integration, or the multi-step reasoning required to compare invoices, purchase orders, and contractual conditions. A "multi-file task in standard mode" is therefore not a supported configuration. Confidence scores do not address document comparison and, notably, are not available in pro mode.
Study Guide alignment: configure Content Understanding analyzers, design structured schemas, process multiple documents, integrate reference data, and implement document validation workflows .


NEW QUESTION # 90
......

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