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SAP C_AIG_2412 Exam Syllabus Topics:
Topic
Details
Topic 1
- 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 evaluated.
Topic 2
- 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 3
- 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 4
- 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.
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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q40-Q45):
NEW QUESTION # 40
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.
- A. The LLM directly ans wers the user's question without accessing external information.
- B. The retrieved content is combined with the LLM's capabilities to generate a response.
- C. The user's questi on is used to search a knowledge base or a set of documents.
- D. The embedding model stores the generated ans wers for future reference.
Answer: B,C
NEW QUESTION # 41
How does the Al API support SAP AI scenarios? Note: There are 2 correct answers to this question.
- A. By managing Kubernetes clusters automatically
- B. By integrating Al services into business applications
- C. By integrating Al models into third-party platforms like AWS
- D. By providing a unified framework for operating Al services
Answer: B,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 # 42
Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core?
Note: There are 2 correct answers to this question.
- A. Grant access rights to the SAP BTP cockpit.
- B. Store the dataset in the SAP HANA Vector Engine.
- C. Store the dataset in a hyperscaler object store.
- D. Provide the storage secret to access the dataset.
Answer: C,D
NEW QUESTION # 43
Which statement best describes the Chain-of-Thought (COT) prompting technique?
- A. Writing a series of connected prompts creating a chain of related information.
- B. Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps.
- C. Connecting related concepts by having the LLM generate chains of ideas.
- D. Linking multiple Al models in sequence, where each model's output becomes the input for the next model in the chain.
Answer: B
Explanation:
Chain-of-Thought (CoT) prompting is a technique that involves concatenating multiple related prompts to guide a language model through a series of reasoning steps, leading to a final conclusion.
1. Structure of CoT Prompting:
* Sequential Reasoning:By breaking down a complex problem into a sequence of intermediate prompts, the model addresses each step methodically, enhancing its problem-solving capabilities.
* Logical Progression:Each prompt builds upon the previous one, ensuring a coherent flow of information that mirrors human logical reasoning.
2. Advantages of CoT Prompting:
* Enhanced Comprehension:This structured approach helps the model understand and process intricate tasks by focusing on one aspect at a time.
* Improved Accuracy:By guiding the model through detailed reasoning steps, CoT prompting reduces the likelihood of errors in the final output.
NEW QUESTION # 44
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 Template and Default Input Values
- C. Run the Orchestration Request
- D. Define the LLM
Answer: D
NEW QUESTION # 45
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