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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement generative AI and agentic solutions30–35%- 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
- Design and implement intelligent agents
  • 1. Select agent architecture patterns
  • 2. Implement multi-agent workflows and orchestration
  • 3. Integrate agents with external systems and data sources
  • 4. Manage state, memory, and context
Topic 2: Implement text and speech analysis solutions10–15%- 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. Build conversational language understanding
  • 3. Perform sentiment analysis, entity recognition, and summarization
Topic 3: 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. Implement Azure AI Search
  • 2. Create and manage vector indexes
  • 3. Design knowledge mining pipelines
Topic 4: Implement computer vision solutions10–15%- Implement image analysis and processing
  • 1. Extract text and structure from images
  • 2. Use Azure AI Vision services
  • 3. Implement object detection and image classification
- Build multimodal solutions
  • 1. Combine vision and language capabilities
  • 2. Process and analyze video content
Topic 5: Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Configure model and agent deployments
  • 3. Monitor and maintain AI workloads
- Design Azure AI infrastructure
  • 1. Plan for security, compliance, and responsible AI
  • 2. Design for scalability, availability, and cost optimization
  • 3. Select appropriate Azure AI Foundry services

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question #1

You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?

  • A. Use speech translation to generate the transcripts into multiple languages.
  • B. Use real-time speech to text to process streaming audio input.
  • C. Run a batch transcription job on recorded audio files.
  • D. Use text to speech by using a custom neural voice.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

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. "auto"
  • B. "required"
  • C. {"type": "bing_grouding"}
  • D. {"type": "azure_ai-search"}
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

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 API key security scheme
  • B. a header parameter defined for each operation
  • C. an Azure Key Vault connection
  • D. a Bearer token security scheme
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?

  • A. evaluation metrics
  • B. latency
  • C. token usage
  • D. run success rate
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

Hotspot Question
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:

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