Which type of compute resource should you use to attach an existing Azure Kubernetes Service (AKS) cluster to Azure Machine Learning?
Answer(s): D
To attach an existing Azure Kubernetes Service (AKS) cluster to Azure Machine Learning, you should use the "Attached Compute" or "Kubernetes Compute Target". This allows you to leverage your existing AKS cluster as a compute resource for your machine learning tasks within Azure Machine Learning. You can achieve this using the Azure CLI v2, Python SDK v2, or Machine Learning Studio UI.1. Kubernetes Compute Target:Azure Machine Learning treats your AKS cluster as a compute target, allowing you to specify it as the location for running your training jobs or deploying models.2. Attached Compute:This refers to the ability to connect existing compute resources, like your AKS cluster, to your Azure Machine Learning workspace.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace
Which Azure AI Document Intelligence prebuilt model should you use to extract parties and jurisdictions from a legal document?
Answer(s): C
The contract model analyzes and extracts key fields and line items from contractual agreements including parties, jurisdictions, contract ID, and title.
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/model-overview
DRAG DROP (Drag and Drop is not supported)You are designing a system that will generate insurance quotes automatically.Match the Microsoft responsible AI principles to the appropriate requirements.To answer, drag the appropriate principle from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all.Note: Each correct match is worth one point.Select and Place:
Answer(s): A
Box 1: TransparencyTransparency: Transparency is crucial in responsible AI. Microsoft aims to provide transparency in how its AI systems operate, allowing users and stakeholders to understand the decision-making processes and outcomes.Box 2: Privacy and SecurityPrivacy and Security: Protecting user privacy and ensuring the security of AI systems are key principles for Microsoft. This involves implementing robust measures to safeguard user data and prevent unauthorized access.Box 3: InclusivenessInclusiveness: Inclusiveness is about ensuring that AI technologies are designed to benefit all users, irrespective of their backgrounds or abilities. Microsoft seeks to avoid creating technologies that unintentionally exclude or disadvantage certain groups.
https://techcommunity.microsoft.com/discussions/azure-ai-services/microsoft-responsible-ai-principles/4037307
You plan to create an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. The solution will be billed via tokens for inputs and outputs to the API.Which deployment option should you use?
Deployment overview for Azure AI Foundry ModelsCapabilities for the deployment optionsThe following table lists details about specific capabilities available for each deployment option:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/deployments-overview
HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Box: Ongoing monitoring, feedback and evaluation____ are goals of the Reliability & Safety principle in the Microsoft Responsible AI Standard Playbook.Ongoing monitoring, feedback, and evaluation are key activities for the Reliability & Safety principle within the Microsoft Responsible AI Standard, which is detailed in the Microsoft Responsible AI Standard General Requirements document. These practices are essential to ensure that AI systems continue to perform reliably, avoid unexpected outcomes, and mitigate risks after deployment.
https://msblogs.thesourcemediaassets.com/sites/5/2022/06/Microsoft-Responsible-AI-Standard-v2-General- Requirements-3.pdf
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