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SAP C-AIG-2412 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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 (Q58-Q63):

NEW QUESTION # 58
Which of the following are features of the SAP AI Foundation? Note: There are 2 correct answers to this question.

A. Al runtimes and lifecycle management
B. Open source Al model repository
C. Joule integration in SAP SuccessFactors
D. Ready-to-use Al services

Answer: A,D

NEW QUESTION # 59
Which of the following executables in generative Al hub works with Anthropic models?

A. GCP Vertex Al
B. Azure OpenAl Service
C. AWS Bedrock
D. SAP AI Core

Answer: C

NEW QUESTION # 60
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

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

Answer: D

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 # 61
Why is generative Al gaining significant attention and investment in the current business landscape? Note: There are 2 correct answers to this question.

A. It only requires natural language skills to use.
B. It can replicate complex technical skills without training or quality control.
C. It can run entire business operations without human intervention.
D. It lowers barriers to adoption.

Answer: A,D

NEW QUESTION # 62
Which of the following steps is NOT a requirement to use the Orchestration service?

A. Create a deployment for orchestration
B. Get an auth token for orchestration
C. Modify the underlying Al models
D. Create an instance of an Al model

Answer: C

NEW QUESTION # 63
......

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