SAP C_AIG_2412 Dumps Questions Answers

The provided code snippet defines a Large Language Model (LLM) within the SAP Generative AI Hub SDK's orchestration service:

from gen_ai_hub.orchestration.models.llm import LLM

llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})

1. Importing the LLM Class:

Code:from gen_ai_hub.orchestration.models.llm import LLM

Purpose:Imports the LLM class from the SDK, enabling the creation of an LLM instance.

2. Defining the LLM Instance:

Code:llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})

Parameters:

name:Specifies the model's name, in this case, "gpt-40".

version:Indicates the model version, set to "latest" to use the most recent version.

parameters:A dictionary defining model-specific parameters:

max_tokens:Sets the maximum number of tokens (words or word pieces) the model can generate, here limited to 256 tokens.

temperature:Controls the randomness of the output; a lower value like 0.2 results in more deterministic responses.

3. Role in Orchestration Pipeline:

Function:This definition is a crucial step in the orchestration pipeline, specifying which LLM to use and configuring its behavior for subsequent tasks.

Conclusion:

The code snippet defines an LLM named "gpt-40" with specific parameters, preparing it for integration into an AI-driven workflow within SAP's Generative AI Hub.

Resource groups in SAP AI Core play a vital role in managing machine learning workloads by offering mechanisms for separation and isolation, which are essential for maintaining efficiency and security.

1. Ensuring Workload Separation for Different Tenants or Departments:

Multitenancy Support:Resource groups enable the segregation of workloads among various tenants or departments within an organization, ensuring that each unit's processes are isolated and managed independently.

Operational Efficiency:This separation prevents interference between workloads, allowing for tailored resource allocation and management strategies that meet the specific needs of each tenant or department.

Prompt managementis the section within SAP's generative AI hub (accessed via the SAP AI Launchpad) where you work with prompts, including:

Creating and saving prompts:You can write and store your prompts for later use.

Viewing prompt history:You can see a record of your past prompts and the AI's responses.

Exporting prompts and responses:This is where you'd find the option to download your prompt and its corresponding response in JSON format.

Why the other options are incorrect:

A. Chat:The chat interface is where you have interactive conversations with the generative AI. While you can see your conversation history there, you wouldn't typically find options to download it in JSON format.

C. Administration:The administration section focuses on managing settings and configurations for the AI hub, not on individual prompts and responses.

D. Prompt Editor:While a prompt editor might be used to create and refine prompts, it wouldn't necessarily be the place to manage or download them.