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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Implement generative AI solutions25-30%- Optimize and evaluate models
  • 1. Evaluate responses and grounding
  • 2. Configure content filters and safety
  • 3. Implement multimodal AI capabilities
- Develop generative AI applications
  • 1. Use Azure OpenAI and Foundry models
  • 2. Build retrieval-augmented generation solutions
  • 3. Implement prompt engineering
Plan and manage Azure AI solutions25-30%- Manage AI solution lifecycle
  • 1. Apply responsible AI practices
  • 2. Monitor model and application performance
  • 3. Implement CI/CD for AI applications
- Plan Azure AI resources
  • 1. Select Azure AI services and Foundry resources
  • 2. Configure authentication and security
  • 3. Manage deployments and monitoring
Implement agentic solutions20-25%- Manage agent operations
  • 1. Implement scalable deployments
  • 2. Secure agent interactions
  • 3. Monitor and debug agents
- Build AI agents
  • 1. Integrate tools and external knowledge
  • 2. Configure memory and orchestration
  • 3. Create autonomous and multi-agent workflows
Implement text analysis and information extraction solutions10-15%- Analyze and extract information
  • 1. Use document intelligence services
  • 2. Extract entities and structured data
  • 3. Implement natural language processing
Implement computer vision solutions10-15%- Analyze visual content
  • 1. Implement OCR and visual understanding
  • 2. Use multimodal vision APIs
  • 3. Process images and video

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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q78-Q83):

NEW QUESTION # 78
You have an Azure subscription.
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
What should you use?

Answer: B

Explanation:
To deploy the DALL-E model, you need Microsoft Foundry and the Azure Command Line Interface (CLI).
Deployment vs. Application Logic: The Azure CLI along with Microsoft Foundry handles the administrative infrastructure layer required to provision, configure, and manage model deployments within your subscription.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/create-resource


NEW QUESTION # 79
You have a large collection of image files and PDF documents stored in an Azure Storage account. The documents contain tabular data.
You need to extract the tables into a structured format that can be imported into a database. The solution must minimize development effort. What should you use?

Answer: B

Explanation:
To extract tables from numerous images and PDF documents in Azure Storage into a structured database with low development, you should utilize Azure AI Document Intelligence (formerly Form Recognizer) within the Azure AI Foundry ecosystem. The Layout Model is best for extracting tables, rows, columns, and headers.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/document-intelligence


NEW QUESTION # 80
A legal team must extract clauses and inferred fields from unstructured contracts. One required field is the contract end date, which is not stated explicitly and must be derived from the start date plus the term. The team has no labelled training data. Which tool best fits?

Answer: B

Explanation:
Azure Content Understanding handles unstructured documents, works zero-shot without labelled data, and can infer fields that are not explicitly present, such as deriving a contract end date from the start date and term. That matches every requirement in the scenario.


NEW QUESTION # 81
You need to create a new resource That will be used to perform sentiment analysis and optical character recognition (OCR}. The solution must meet the following requirements:
* Use a single key and endpoint to access multiple services.
* Consolidate billing for future services that you might use.
* Support the use of Azure Vision in Foundry Tools in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Azure Resource Manager uses an HTTP PUT request to create or update a resource at a specified resource ID. Therefore, the first dropdown must be PUT.
The resource must use the CognitiveServices kind because the requirements describe a multi-service Azure AI resource rather than an individual service-specific resource.
A TextAnalytics-specific kind would provide Language functionality but would not satisfy the requirement for a single resource covering OCR and future Vision use. ComputerVision would similarly focus on Vision rather than consolidating multiple Azure AI service capabilities.
The multi-service resource allows applications to access supported Azure AI services while centralizing resource administration and billing. A single Azure AI services resource can therefore serve as the account boundary for capabilities such as Language sentiment analysis and Vision OCR.
The request should target the Microsoft.CognitiveServices/accounts resource type and supply the selected kind and SKU in the body. Identity configuration may additionally be applied depending on the security architecture.
The AI-103 Study Guide requires candidates to design Azure infrastructure for AI apps and choose appropriate Foundry services as part of planning and managing an Azure AI solution. Microsoft Learn


NEW QUESTION # 82
You have a Microsoft Foundry project named Project1 that contains the following:
* An OpenAPI tool that calls an external API
* A project connection named Connection1 that stores the API key of the external API When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?

Answer: B


NEW QUESTION # 83
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