Microsoft AI-103 Mock Test - New AI-103 Exam Book

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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Implement Azure AI Search
  • 3. Create and manage vector indexes
Topic 2: 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. Extract text and structure from images
  • 2. Implement object detection and image classification
  • 3. Use Azure AI Vision services
Topic 3: Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Select agent architecture patterns
  • 3. Manage state, memory, and context
  • 4. Implement multi-agent workflows and orchestration
- Build generative AI applications
  • 1. Integrate Azure OpenAI and other models
  • 2. Implement function calling and tool use
  • 3. Build retrieval-augmented generation (RAG) solutions
  • 4. Implement prompt engineering and optimization
Topic 4: 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. Select appropriate Azure AI Foundry services
  • 2. Design for scalability, availability, and cost optimization
  • 3. Plan for security, compliance, and responsible AI
Topic 5: Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Use Azure AI Language services
  • 2. Perform sentiment analysis, entity recognition, and summarization
  • 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 (Q37-Q42):

NEW QUESTION # 37
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?

Answer: B


NEW QUESTION # 38
You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of all the files that can be uploaded to the service?

Answer: C

Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/agents/quotas-limits


NEW QUESTION # 39
You have an invoice-processing application named App1 that uses Azure Constant 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 voice against static vendor contact 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:
Multiple-file task: Required over a single-file task. Your pipeline needs to reconcile data across two separate active transactional documents (the invoice and the purchase order) within a single analyzer request.
Pro mode is required instead of standard mode. Pro mode is specifically designed for advanced scenarios requiring multi-step reasoning, cross-file analysis, and validation against a knowledge base. Note that Pro mode currently supports classify and generate fields but does not support confidence scores for specific extracted fields.
Vendor contract files as reference data: Required. Because the vendor contracts are static compliance documents, they should be uploaded and treated as the analyzer's background knowledge base (reference data) to guide the validation logic.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/standard-pro-modes


NEW QUESTION # 40
You need to optimize the reading experience for users with reduced comprehension and learning differences such as dyslexia.

Answer: D

Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Azure AI Immersive Reader is specifically designed to improve reading accessibility and comprehension while requiring minimal custom implementation.
Immersive Reader provides an integrated reading experience with features intended to support readers with dyslexia, language learners, emerging readers, and users who benefit from reduced visual or cognitive complexity. Capabilities include read-aloud functionality, adjustable text spacing, line focus, syllable separation, parts-of-speech highlighting, text sizing, and other presentation controls.
Azure Language performs NLP tasks such as entity extraction, sentiment analysis, classification, and summarization; it does not provide a complete accessibility-oriented reading interface. Translator changes text from one language to another but does not provide dyslexia-focused reading controls. Document Intelligence extracts text and structure from documents but similarly does not provide the end-user reading experience.
Because the application can embed a specialized existing component instead of implementing individual accessibility functions, Immersive Reader satisfies the "minimize development effort" requirement.
This scenario also reflects Microsoft ' s responsible-AI principle of inclusiveness , although the exam objective is operationally centered on selecting the appropriate Azure capability for an accessible language experience.


NEW QUESTION # 41
You need to ensure that the marketing department can generate videos by using the model deployed to Project2. now should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Call client.videos.create() to submit the prompt and start a video-generation job against the model deployment. Video generation is asynchronous: the initial response represents a job and contains properties such as its unique ID and current status rather than immediately returning the completed video. Microsoft's official Python example creates the job by using client.videos.create(model=..., prompt=...).
The application must then poll the job. After waiting between requests, client.videos.retrieve(video.id) obtains the latest state of that specific job. Polling continues until the status becomes completed, failed, or cancelled.
list would enumerate jobs rather than refresh the selected job, while download_content is used only after successful completion to retrieve the generated video file.
To comply with Contoso's prohibition on API keys, the client should use a Microsoft Entra bearer-token provider. The OpenAI Python client accepts this provider through its api_key constructor parameter even though no static API key is used.
Study Guide alignment: design and implement video-generation solutions and integrate generative workflows through Foundry SDKs.


NEW QUESTION # 42
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