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SAP C-AIG-2412 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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.

>> Exam C-AIG-2412 Topic <<

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q21-Q26):

NEW QUESTION # 21
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 provide isolation for datasets and Al artifacts.
B. They ensure workload separation for different tenants or departments.
C. They enhance pipeline execution speeds through workload distribution.
D. They enable simultaneous orchestration of Kubernetes clusters.

Answer: A,B

NEW QUESTION # 22
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system?
Note: There are 2 correct answers to this question.

A. Relevance
B. Carbon footprint
C. Speed
D. Faithfulness

Answer: A,D

NEW QUESTION # 23
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. Generate JSON strings based on extracted information.
B. Train custom models based on the mail data.
C. Generate a new SAP application based on the mail data.
D. Generate random email content and send them to customers.

Answer: A

NEW QUESTION # 24
What is the primary function of the generative Al hub in SAP's Al Foundation?

A. To manage the Al lifecycle efforts end-to-end
B. To serve as an abstraction layer to access a range of foundation Al models
C. To provide ready-to-use Al services for document processing
D. To store embeddings of unstructured data for semantic data retrieval

Answer: B

NEW QUESTION # 25
What is the primary function of the embedding model in a RAG system?

A. To generate responses based on retrieved documents and user queries
B. To encode queries and documents into vector representations for comparison
C. To store vector representations of documents and search for relevant passages
D. To evaluate the faithfulness and relevance of generated Answers

Answer: B

Explanation:
In a Retrieval-Augmented Generation (RAG) system, the embedding model plays a crucial role in encoding textual data into vector representations, facilitating efficient retrieval and comparison.
1. Function of the Embedding Model:
* Vector Encoding:The embedding model transforms both user queries and documents into high- dimensional vector representations. This numerical encoding captures the semantic meaning of the text, enabling the system to assess similarities between different pieces of text effectively.
* Facilitating Retrieval:By encoding text into vectors, the system can perform efficient similarity searches within a vector database, identifying documents or passages that are most relevant to the user's query.
2. Importance in RAG Systems:
* Semantic Matching:The vector representations allow the system to match user queries with relevant documents based on semantic content rather than mere keyword overlap, enhancing the relevance of retrieved information.
* Efficiency:Vector-based retrieval is computationally efficient, enabling rapid identificationof pertinent information from large datasets, which is essential for real-time applications.
3. Application in SAP's Generative AI Hub:
* Integration with HANA Vector Search:SAP's Generative AI Hub integrates embedding models with HANA's vector search capabilities, allowing for efficient storage and retrieval of vector embeddings.
This integration supports the development of RAG systems that can effectively utilize SAP's data assets.
* Generative AI Hub SDK:SAP provides an SDK that facilitates the implementation of embedding models within RAG systems, enabling developers to encode queries and documents into vector representations seamlessly.

NEW QUESTION # 26
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