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| Section | Weight | Objectives |
|---|
| Implement agentic solutions | 20-25% | - Manage agent operations
- 1. Monitor and debug agents
- 2. Secure agent interactions
- 3. Implement scalable deployments
- Build AI agents
- 1. Integrate tools and external knowledge
- 2. Configure memory and orchestration
- 3. Create autonomous and multi-agent workflows
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
- 1. Use multimodal vision APIs
- 2. Implement OCR and visual understanding
- 3. Process images and video
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
- 1. Manage deployments and monitoring
- 2. Configure authentication and security
- 3. Select Azure AI services and Foundry resources
- Manage AI solution lifecycle
- 1. Apply responsible AI practices
- 2. Monitor model and application performance
- 3. Implement CI/CD for AI applications
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
- 1. Use document intelligence services
- 2. Implement natural language processing
- 3. Extract entities and structured data
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
- 1. Implement prompt engineering
- 2. Use Azure OpenAI and Foundry models
- 3. Build retrieval-augmented generation solutions
- Optimize and evaluate models
- 1. Configure content filters and safety
- 2. Evaluate responses and grounding
- 3. Implement multimodal AI capabilities
|
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Microsoft Developing AI Apps and Agents on Azure Sample Questions (Q85-Q90):
NEW QUESTION # 85
You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
- A. Use real-time speech to text to process streaming audio input.
- B. Run a batch transcription job on recorded audio files.
- C. Use text to speech by using a custom neural voice.
- D. Use speech translation to generate the transcripts into multiple languages.
Answer: A
NEW QUESTION # 86
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
- A. Provision an Application Insights resource.
- B. Create a Microsoft Foundry project.
- C. Add Agent1 to the Microsoft Foundry playground.
- D. Add Agent1to a Microsoft Foundry project.
Answer: B
NEW QUESTION # 87
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
- A. the endpoint URI and subscription key
- B. the endpoint URI and a shared access signature (SAS) token
- C. the endpoint URI and an OAuth token
- D. a system assigned managed identity and an X.509 certificate
Answer: A
Explanation:
To configure the App Service app to access the Microsoft Foundry Service (part of Azure AI Services) with low administration, you need to configure the app with the endpoint URI and a subscription key.
These credentials can be found in the Azure Portal under the Keys & Endpoint section of your provisioned Foundry resource, allowing the app to seamlessly authenticate API calls.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/authentication
NEW QUESTION # 88
You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1.
What should you include in the recommendation?
- A. Grounding with Bing Search
- B. Azure Document Intelligence in Foundry Tools
- C. Azure Al Search
- D. Azure Translator in Foundry Tools
Answer: C
NEW QUESTION # 89
A copilot must answer multi-part questions that depend on earlier turns in the conversation. You want the system to break each complex question into focused subqueries, run them in parallel, and semantically rerank the results before composing an answer. Which Azure AI Search capability provides this?
- A. Classic keyword search with a scoring profile
- B. Agentic retrieval
- C. Increasing the top-k value on a single keyword query
- D. A single vector query with a high similarity threshold
Answer: B
Explanation:
Agentic retrieval in Azure AI Search uses a large language model to decompose a complex query into focused subqueries, runs them in parallel, semantically reranks each set of results, and merges them into a unified response, while taking conversation history into account. That is precisely the behaviour the scenario describes.
NEW QUESTION # 90
......
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