Microsoft Developing AI Apps and Agents on Azure : AI-103

AI-103 real exams

Exam Code: AI-103

Exam Name: Developing AI Apps and Agents on Azure

Updated: Aug 08, 2026

Q & A: 159 Questions and Answers

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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Monitor and maintain AI workloads
  • 2. Configure model and agent deployments
  • 3. Integrate with CI/CD pipelines
- 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
Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Build conversational language understanding
  • 2. Perform sentiment analysis, entity recognition, and summarization
  • 3. Use Azure AI Language services
- Implement speech capabilities
  • 1. Speech-to-text and text-to-speech integration
  • 2. Speech translation and speaker recognition
Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Combine vision and language capabilities
  • 2. Process and analyze video content
- Implement image analysis and processing
  • 1. Implement object detection and image classification
  • 2. Extract text and structure from images
  • 3. Use Azure AI Vision services
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Select agent architecture patterns
  • 2. Implement multi-agent workflows and orchestration
  • 3. Manage state, memory, and context
  • 4. Integrate agents with external systems and data sources
- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Build retrieval-augmented generation (RAG) solutions
  • 3. Integrate Azure OpenAI and other models
  • 4. Implement function calling and tool use
Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Implement Azure AI Search
  • 2. Design knowledge mining pipelines
  • 3. Create and manage vector indexes
- Extract structured data from documents
  • 1. Use Azure AI Document Intelligence
  • 2. Process forms, invoices, and unstructured content

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Summarizes documents that contain multiple sentences
- Preserves the original sentence order
- Returns exactly three sentences
Which Language service feature should you use?

A) extractive summarization
B) key phrase extraction
C) abstractive summarization
D) sentiment analysis


2. You have a Microsoft Foundry agent that grounds responses from an Azure Search index that contains the following:
- Searchable text fields for product names and product codes
- A vector field that stores embeddings for product descriptions
You need to ensure that users can query the index by using the following:
- Exact product names or codes
- Natural language descriptions of the products
What should you configure?

A) semantic search only
B) hybrid search
C) keyword search only
D) vector search only


3. You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per- field confidence score and source grounding to locations within the source document.
What should you do?

A) Enable estimateFieldSourceAndConfidence.
B) Set enableSegment to true.
C) Configure the analyzer to use generative extraction for all fields.
D) Provide labeled samples.


4. You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?

A) the custom speech project ID
B) the project ID
C) the project URL
D) the custom speech endpoint URL


5. You have an Azure AI Search indexer that ingests PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?

A) Azure Content Understanding in Foundry Tools
B) Document Layout
C) Document Extraction
D) GenAI Prompt


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B
Question # 3
Answer: A
Question # 4
Answer: C
Question # 5
Answer: A

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