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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 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.

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

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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q36-Q41):

NEW QUESTION # 36
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

A. To enable the matching of different relevant passages to user queries
B. To improve the efficiency of encoding queries into vector representations
C. To reduce the storage space required for the vector database
D. To simplify the process of training the embedding model

Answer: A

Explanation:
In Retrieval-Augmented Generation (RAG) systems, splitting documents into smaller overlapping chunks is a crucial preprocessing step that enhances the system's ability to match relevant passages to user queries.
1. Purpose of Splitting Documents into Smaller Overlapping Chunks:
* Improved Retrieval Accuracy:Dividing documents into smaller, manageable segments allows the system to retrieve the most relevant chunks in response to a user query, thereby improving the precision of the information provided.
* Context Preservation:Overlapping chunks ensure that contextual information is maintained across segments, which is essential for understanding the meaning and relevance of each chunk in relation to the query.
2. Benefits of This Approach:
* Enhanced Matching:By having multiple overlapping chunks, the system increases the likelihood that at least one chunk will closely match the user's query, leading to more accurate and relevant responses.
* Efficient Processing:Smaller chunks are easier to process and analyze, enabling the system to handle large documents more effectively and respond to queries promptly.

NEW QUESTION # 37
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 user's questi on is used to search a knowledge base or a set of documents.
C. The retrieved content is combined with the LLM's capabilities to generate a response.
D. The embedding model stores the generated ans wers for future reference.

Answer: B,C

Explanation:
Retrieval-Augmented Generation (RAG) is a process that enhances the capabilities of Large Language Models (LLMs) by integrating external knowledge sources into the response generation process.
1. Understanding the RAG Process:
* User Query:The process begins with a user's question or prompt, which serves as the input for the system.
* Retrieval Step:The system uses the user's query to search a knowledge base or a set of documents, retrieving relevant information that can inform the response.
* Integration with LLM:The retrieved content is then combined with the LLM's inherent knowledge and language generation capabilities to produce a comprehensive and contextually relevant response.
2. Benefits of the RAG Process:
* Enhanced Accuracy:By incorporating up-to-date and domain-specific information from external sources, RAG improves the accuracy of AI-generated responses.
* Contextual Relevance:The integration of retrieved data ensures that the responses are more aligned with the specific context of the user's query.
3. Application in SAP's Generative AI Hub:
* Generative AI Hub SDK:SAP provides a Generative AI Hub SDK that facilitates the implementation of RAG by enabling seamless integration of retrieval mechanisms with LLMs.
* Tutorials and Resources:SAP offers tutorials, such as "Retrieval Augmented Generation using generative-ai-hub-sdk and HANA vector search," to guide developers in implementing RAG systems effectively.

NEW QUESTION # 38
Which neural network architecture is primarily used by LLMs?

A. Transformer architecture with self-attention mechanisms
B. Recurrent neural network architecture
C. Sequential encoder-decoder architecture
D. Convolutional Neural Networks (CNNs)

Answer: A

NEW QUESTION # 39
Which of the following are functionalities provided by the generative-Al-hub-SDK ?
Note: There are 2 correct answers to this question.

A. Create chat responses and embeddings
B. Interact with LLMs
C. Configure SAP BTP credentials
D. Customize SAP AI Launchpad

Answer: A,B

NEW QUESTION # 40
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note:
There are 2 correct answers to this question.

A. They ensure workload separation for different tenants or departments.
B. They enable simultaneous orchestration of Kubernetes clusters.
C. They provide isolation for datasets and Al artifacts.
D. They enhance pipeline execution speeds through workload distribution.

Answer: A,C

Explanation:
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.

NEW QUESTION # 41
......

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