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

AI-103 Exam Simulator
  • Exam Code: AI-103
  • Exam Name: Developing AI Apps and Agents on Azure
  • Updated: Oct 27, 2026
  • Q & A: 152 Questions and Answers

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Microsoft AI-103 Exam Syllabus Topics:

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

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question #1

You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.

Question #2

You need to extract prebuilt entity categories including Person, Organization, Location, and DateTime from each email without training a model.

  • A. Configure NamedEntityRecognitionSkill in a skillset.
  • B. Call recognize_pii_entities.
  • C. Submit an analyze job that uses Custom Entity Recognition.
  • D. Call recognize_entities.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?

  • A. Split the mixed-language segments into single-language segments and translate each segment separately.
  • B. Specify English as the source language in the translation request for all the segments.
  • C. Use document translation to translate the entire transcript as a single document.
  • D. Enable automatic language detection for the translation request.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

You need to optimize content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Moderate text content feature to run the tests.
Does this meet the requirement?

  • A. Yes
  • B. No
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

You have an Azure Al Search resource named Search1 that is used by multiple apps hosted in Azure. You need to secure Search1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to query specific indexes.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
* To prevent access from the internet: Create a private endpoint
* To limit access to query specific indexes: Use Azure roles
Create an Azure Private Link private endpoint for Search1 and disable public network access. The private endpoint assigns Search1 a private IP address within an Azure virtual network, allowing the hosted applications to reach the search service over the Microsoft backbone instead of through its public endpoint.
Disabling public network access ensures that requests originating from the public internet are rejected. An IP firewall would still expose the public endpoint to approved public IP addresses and therefore would not provide complete internet isolation.
For index-level authorization, enable Microsoft Entra ID role-based access control and assign Azure roles to each application's managed identity or service principal. The Search Index Data Reader role permits query and retrieval operations without allowing index modification. Azure AI Search supports scoping Search Index Data Reader or Search Index Data Contributor permissions to an individual index; custom role definitions can also be used when more precise permissions are required.
Query-key authentication is unsuitable because API keys normally provide service-level access and cannot reliably isolate applications to designated indexes.
Study Guide alignment: secure Azure AI services by configuring private endpoints, network isolation, managed identities, Microsoft Entra authentication, and role-based access control.

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