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| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
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NEW QUESTION # 73
A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?
Answer: D
Explanation:
Serverless API deployment runs on Microsoft-managed infrastructure, bills per input and output token, and needs no VM quota, so it is the fastest path that matches token-based billing. It is the standard choice when you want pay-per-use economics without managing compute.
NEW QUESTION # 74
You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
* An if/else condition expression that ensures that Var01 contains a value
* A Send message expression that returns the stored user response in uppercase How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
If/else condition expression: Not(IsBlank(Local.Var01))
Send message expression: {Upper(Local.Var01)}
The correct if/else condition is Not(IsBlank(Local.Var01)) because the workflow must continue only when the Ask a question node has captured a nonblank value. In Power Fx, IsBlank checks whether a value is blank, while Not reverses that Boolean result; therefore, Not(IsBlank(Local.Var01)) evaluates to true only when Var01 contains user input. IsEmpty(Local.Var01) is not the correct choice because IsEmpty checks whether a table contains records, not whether a text variable has a value. The Power Fx formula reference distinguishes these functions by defining IsBlank for blank values and IsEmpty for empty tables.
The Send message expression must be {Upper(Local.Var01)}. Microsoft Foundry workflow guidance uses the same pattern for an Ask a question node saved as Var01, followed by a Send message action that outputs
{Upper(Local.Var01)}. The Local. prefix is required because the response is stored in a local workflow variable, and Upper() converts the stored text response to uppercase. Reference topics: Microsoft Foundry workflows, Power Fx expressions, local variables, if/else branching, and Send message actions.
NEW QUESTION # 75
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?
Answer: B
NEW QUESTION # 76
You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support real-time inference. The solution must meet the following requirements:
- Use key-based authentication.
- Support real-time REST API access.
- NOT consume the vCPU quota of the virtual machines in the Azure
subscription.
Which type of deployment should you use?
Answer: A
Explanation:
The most appropriate type of deployment is a serverless API deployment (also referred to as a standard deployment or Models-as-a-Service / MaaS).
Key-Based Authentication: Serverless API deployments natively provision an endpoint URL alongside primary and secondary API keys to secure your client applications.
Real-Time REST API Access: When the model is successfully deployed, it exposes a scalable, real-time HTTP/REST endpoint matching the standardized Azure AI Model Inference API.
No Virtual Machine vCPU Quota Consumption: Unlike managed compute deployments-which provision dedicated virtual machines in your subscription and require VM vCPU quota- serverless API deployments are completely hosted and managed by Microsoft. They operate on a pay-as-you-go, token-based billing structure and do not consume any VM vCPU quota from your Azure subscription.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/deployments-overview
NEW QUESTION # 77
You are building an image sharing app that will use Azure AI to prevent users from sharing sexually explicit images.
You need to ensure that inappropriate images are identified correctly. The solution must minimize development effort.
What should you use?
Answer: D
Explanation:
To prevent users from sharing sexually explicit images, you need a tool that can handle content moderation specifically designed for inappropriate content such as adult or sexually explicit material. Azure AI Content Safety Studio is a service designed for this purpose, providing pre- built AI models that can detect inappropriate content like adult images, violence, and other sensitive material. It minimizes development effort by offering an easy-to-use, pre-configured service for content safety without the need to build custom models.
NEW QUESTION # 78
......
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