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[General] SAP C-AIG-2412 Valid Test Forum, Free C-AIG-2412 Learning Cram

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【General】 SAP C-AIG-2412 Valid Test Forum, Free C-AIG-2412 Learning Cram

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q22-Q27):NEW QUESTION # 22
Which technique is used to supply domain-specific knowledge to an LLM?
  • A. Domain-adaptation training
  • B. Prompt template expansion
  • C. Fine-tuning the model on general data
  • D. Retrieval-Augmented Generation
Answer: A

NEW QUESTION # 23
You want to use the orchestration service through SAP's generative-Al-hub-sdk. What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM 11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
  • A. Define the LLM
  • B. Run the Orchestration Request
  • C. Create the Orchestration Configuration
  • D. Define the Template and Default Input Values
Answer: A
Explanation:
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.

NEW QUESTION # 24
How does the Al API support SAP AI scenarios? Note: There are 2 correct answers to this question.
  • A. By providing a unified framework for operating Al services
  • B. By managing Kubernetes clusters automatically
  • C. By integrating Al models into third-party platforms like AWS
  • D. By integrating Al services into business applications
Answer: A,D
Explanation:
The AI API in SAP plays a crucial role in supporting AI scenarios by facilitating the integration of AI services into business applications and providing a unified framework for operating these services.
1. Integration of AI Services into Business Applications:
* Seamless Integration:The AI API enables developers to incorporate AI functionalities directly into SAP and non-SAP business applications, enhancing their capabilities with intelligent features.
* Use Case Example:Integrating AI models into SAP Build Apps allows for the creation of AI-powered applications that can perform tasks such as data analysis, predictive modeling, and more.
2. Unified Framework for Operating AI Services:
* Standardized Operations:The AI API provides a consistent and standardized framework for managing AI services, ensuring uniformity in deployment, monitoring, and maintenance across different platforms.
* Scalability and Flexibility:This unified approach allows for scalable AI operations, accommodating various AI models and services within a cohesive operational structure.

NEW QUESTION # 25
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note:
There are 2 correct answers to this question.
  • A. Faithfulness
  • B. Speed
  • C. Relevance
  • D. Carbon footprint
Answer: A,C
Explanation:
Evaluating the effectiveness of a Retrieval-Augmented Generation (RAG) system involves assessing specific metrics that determine the quality and reliability of the generated content.
1. Faithfulness:
* Definition:Faithfulness measures the degree to which the generated output accurately reflects the information retrieved from source documents without introducing unsupported content.
* Importance:High faithfulness ensures that the system's responses are trustworthy and based on factual data, which is crucial for applications requiring precise information dissemination.
2. Relevance:
* Definition:Relevance assesses how pertinent the generated content is to the user's query or the task at hand.
* Importance:Ensuring relevance guarantees that the system provides information that directly addresses user needs, enhancing user satisfaction and system utility.
3. Application in RAG Systems:
* Performance Evaluation:By measuring faithfulness and relevance, developers can fine-tune RAG systems to produce outputs that are both accurate and pertinent, thereby improving overall system performance.
* User Trust:Maintaining high levels of these metrics fosters user trust, as the system consistently delivers reliable and contextually appropriate information.

NEW QUESTION # 26
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.
  • A. It only supports traditional machine learning models.
  • B. It provides instant access to a wide range of large language models (LLMs).
  • C. It ensures relevant, reliable, and responsible business Al.
  • D. It operates independently of SAP's partners and ecosystem.
Answer: B,C

NEW QUESTION # 27
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