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NEW QUESTION # 113
Which type of compute resource should you use to attach an existing Azure Kubernetes Service (AKS) cluster to Azure Machine Learning?
Answer: A
Explanation:
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.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace
NEW QUESTION # 114
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 115
You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user:
- Accept customer orders.
- Retrieve support documents.
- Retrieve order status updates.
Which type of NLP should you use?
Answer: C
Explanation:
The correct choice is language understanding (also termed language modeling).
Language understanding allows a system to determine a user's intent from their natural language input. In this scenario, the chatbot needs to look at a text string and map it to one of three specific operational categories (intents):
Accepting a customer order
Retrieving support documentation
Checking an order status update
NEW QUESTION # 116
You are deploying a generative AI model to a Microsoft Foundry project.
You assign a higher tokens per minute (TPM) allocation to the model.
What is the result of this change?
Answer: A
Explanation:
A higher Tokens Per Minute (TPM) allocation to a generative AI model in a Microsoft Foundry project increases the project's maximum throughput capacity, allowing it to process more text per minute, handle concurrent users or larger prompts without throttling, and scale production workloads.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/quotas-limits
NEW QUESTION # 117
Your company provides users with clear information about an AI system's purpose, functionality, and limitations. Which Microsoft responsible AI principle is this an example of?
Answer: C
Explanation:
Transparency is the Microsoft responsible AI principle that provides clear information about an AI system's purpose, how it works, and its limitations.
System Purpose: Explains what the AI is designed to do and how it helps users.
Functionality: Shows how the model makes decisions or generates content.
Limitations: Warns users about potential errors, blind spots, or times when the AI might be wrong.
User Awareness: Ensures people know they are interacting with an AI system.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach
NEW QUESTION # 118
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