Microsoft AI-103 Questions & Answers - in .pdf
- Total Q&A: 159
- Update: Aug 02, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: AI-103
- Exam Name: Developing AI Apps and Agents on Azure
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- Complete Microsoft Recommended Syllabus.
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- Exact Microsoft AI-103 Exam Questions with Correct Answers, verified by Experts with years of Experience in IT Field.
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Microsoft AI-103 Q&A - Testing Engine
- Total Q&A: 159
- Update: Aug 02, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: AI-103
- Exam Name: Developing AI Apps and Agents on Azure
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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze visual content
- 1. Implement OCR and visual understanding
- 2. Use multimodal vision APIs
- 3. Process images and video
|
| Topic 2: Implement generative AI solutions | 25-30% | - Develop generative AI applications
- 1. Build retrieval-augmented generation solutions
- 2. Use Azure OpenAI and Foundry models
- 3. Implement prompt engineering
- Optimize and evaluate models
- 1. Implement multimodal AI capabilities
- 2. Evaluate responses and grounding
- 3. Configure content filters and safety
|
| Topic 3: Implement agentic solutions | 20-25% | - Manage agent operations
- 1. Monitor and debug agents
- 2. Implement scalable deployments
- 3. Secure agent interactions
- Build AI agents
- 1. Create autonomous and multi-agent workflows
- 2. Integrate tools and external knowledge
- 3. Configure memory and orchestration
|
| Topic 4: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
- 1. Use document intelligence services
- 2. Extract entities and structured data
- 3. Implement natural language processing
|
| Topic 5: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
- 1. Apply responsible AI practices
- 2. Implement CI/CD for AI applications
- 3. Monitor model and application performance
- Plan Azure AI resources
- 1. Configure authentication and security
- 2. Manage deployments and monitoring
- 3. Select Azure AI services and Foundry resources
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. A customer support agent must answer from an internal knowledge base that is updated weekly with new product documentation, and its answers must always reflect the latest content. Which approach should you implement?
A) Implement retrieval-augmented generation (RAG) over an index of the documents
B) Apply Direct Preference Optimization (DPO) to the documents
C) Raise the model temperature to improve recall of recent facts
D) Fine-tune the base model on the new documents every week
2. You have a Microsoft Foundry project that contains three agents as shown in the following table.
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?
A) a workflow
B) threads and runs without a workflow
C) separate agent runs coordinated in the application code
D) a multi-agent group chat session
3. You have a Microsoft Foundry project that contains an agent.
The agent ingests scanned PDF vendor invoices that contain tables and embedded QR codes.
The agent must preserve the PDF layout in the extracted output to ensure that downstream processing can reference sections and tables.
You plan to call Azure Content Understanding in Foundry Tools.
You need to extract content and layout elements and detect QR codes without requiring a language model deployment.
Which built-in analyzer should you use?
A) prebuilt-documentSearch
B) prebuilt-read
C) prebuilt-documentFieldSchema
D) prebuilt-layout
4. You have a web app named App1 that processes user prompts by integrating with a Microsoft Foundry project named Project1. App1 performs the following actions:
- Sends prompts directly to a model by using the Azure OpenAI Responses API
- Invokes the Azure AI Content Safety tool by using a Foundry
connection within the same request
You need to configure end-to-end visibility into each step of the request workflow.
What should you do?
A) Enable application tracing in Project1.
B) Route requests through the Azure OpenAI endpoint.
C) Enable logging by using the client SDK for Content Safety.
D) Enable logging by using Foundry Local.
5. You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project.
User messages can contain both text and images.
Currently, App1 includes image URL: as plain text inside the message content so the model cannot recognize them as images.
Traces show that the requests contain a single text message instead of a multimodal content array.
You need to send the message as a structured array that includes both the text portion and the image reference to ensure that the model can process the image correctly.
What should you do?
A) Add the image URL to the request metadata section, so the model can resolve the processing issue automatically.
B) Set the user message content array to include items that have type: text and type: image_url.
C) Encode the image to base64 and include the encoded data inside the content string of the user message.
D) Place the image URL inside the System Message and set type to image_url so the model loads the image at initialization.
Solutions:
Question # 1
Answer: A | Question # 2
Answer: A | Question # 3
Answer: D | Question # 4
Answer: A | Question # 5
Answer: B |
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