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SAP C-AIG-2412 Exam Syllabus Topics:

Topic	Details
Topic 1	Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the evolution of large language models, distinguishing them from traditional IT operations analytics. It also explores the current stages of AIOps systems and their implications for organizations. A key skill assessed is understanding the foundational concepts behind LLMs and their applications in various contexts.
Topic 2	SAP's Generative AI Hub: This section of the exam measures the skills of technology strategists and covers the functionalities provided by SAP's Generative AI Hub. It emphasizes how organizations can use generative AI to create new content and automate complex tasks. A vital skill evaluated is applying generative AI techniques to enhance business processes and customer experiences.
Topic 3	SAP Business AI: This section of the exam measures the skills of business analysts and covers the features and capabilities of SAP Business AI. It includes exploring how AI can automate processes, provide real-time insights, and enhance decision-making across various business functions.
Topic 4	SAP AI Core: This section of the exam measures the skills of SAP developers and covers the core components of SAP's AI framework. It emphasizes how these components integrate with existing systems to enhance functionality and performance. Leveraging SAP AI Core to develop intelligent applications that meet business needs is a critical skill that needs to be evaluated.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q24-Q29):

NEW QUESTION # 24
What are some benefits of SAP Business Al? Note: There are 3 correct answers to this question.

A. Intelligent business document processing
B. Personalized recommendations based on Al algorithms
C. Automatic human emotion recognition
D. Al-powered forecasting and predictions
E. Face detection and face recognition

Answer: A,B,D

NEW QUESTION # 25
How does SAP deal with vulnerability risks created by generative Al? Note: There are 2 correct answers to this question.

A. By relying on external vendors to manage security threats.
B. By identifying human, technical, and exfiltration risks through an Al Security Taskforce.
C. By implementing responsible Al use guidelines and strong product security standards.
D. By focusing on technological advancement only.

Answer: B,C

Explanation:
SAP addresses vulnerability risks associated with generative AI through a comprehensive strategy:
1. Implementation of Responsible AI Use Guidelines and Strong Product Security Standards:
* AI Ethics Policy:SAP has established an AI Ethics Policy that mandates responsible AI usage, ensuring that AI systems are designed and deployed ethically, with considerations for fairness, transparency, and accountability.
* Product Security Standards:SAP integrates robust security measures into its AI products, adhering to stringent security protocols to protect against vulnerabilities and potential threats.
2. Identification of Risks through an AI Security Taskforce:
* AI Security Taskforce:SAP has established an AI Security Taskforce dedicated to identifying and mitigating risks associated with generative AI, including human factors, technical vulnerabilities, and data exfiltration threats.

NEW QUESTION # 26
What are some drivers for the rapid adoption of generative AI? Note: There are 2 correct answers to this question.

A. Ease of use
B. Wide availability
C. Significant hardware cost savings
D. Availability of skilled developers

Answer: A,B

Explanation:
The rapid adoption of generative AI is driven by several key factors:
1. Wide Availability:
* Access to Generative AI Tools:The proliferation of generative AI tools and platforms has made the technology widely accessible to businesses and individuals. This widespread availability facilitates experimentation and integration into various applications.
2. Ease of Use:
* User-Friendly Interfaces:Modern generative AI tools often feature intuitive interfaces that require minimal technical expertise, enabling a broader range of users to leverage AI capabilities without deep technical knowledge.

NEW QUESTION # 27
You want to extract useful information from customer emails to augment existing applications in your company.
How can you use generative-ai-hub-sdk in this context?

A. Train custom models based on the mail data.
B. Generate a new SAP application based on the mail data.
C. Generate random email content and send them to customers.
D. Generate JSON strings based on extracted information.

Answer: D

NEW QUESTION # 28
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. Create the Orchestration Configuration
B. Define the LLM
C. Run the Orchestration Request
D. Define the Template and Default Input Values

Answer: B

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 # 29
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