EXAM C-AIG-2412 OBJECTIVES PDF & C-AIG-2412 DOWNLOAD

Exam C-AIG-2412 Objectives Pdf & C-AIG-2412 Download

Exam C-AIG-2412 Objectives Pdf & C-AIG-2412 Download

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q43-Q48):

NEW QUESTION # 43
What is a Large Language Model (LLM)?

  • A. A database system optimized for storing large volumes of textual data.
  • B. A gradient boosted decision tree algorithm for predicting text.
  • C. A rule-based expert system to analyze and generate grammatically correct sentences.
  • D. An Al model that specializes in processing, understanding, and generating human language.

Answer: D

Explanation:
A Large Language Model (LLM) is an advanced AI model designed to handle various natural language processing tasks.
1. Definition and Purpose:
* Processing:LLMs analyze human language to understand syntax, semantics, and context.
* Understanding:They interpret the meaning behind text, enabling comprehension of nuanced language elements.
* Generating:LLMs can produce coherent and contextually appropriate text, facilitating tasks like content creation and translation.
2. Characteristics of LLMs:
* Scale:These models are trained on vast datasets, encompassing billions of words, which enhances their language capabilities.
* Architecture:LLMs typically utilize complex neural network architectures, such as transformers, to manage and process language data effectively.
3. Applications:
* Content Generation:Creating articles, summaries, and reports.
* Language Translation:Converting text from one language to another with high accuracy.
* Conversational Agents:Powering chatbots and virtual assistants to interact with users naturally.


NEW QUESTION # 44
What are some components of the training pipeline in SAP AI Core?
Note: There are 2 correct answers to this question.

  • A. Executables that define the training process
  • B. Input datasets stored in a hyperscaler object store
  • C. Automated deployment to Kubernetes clusters
  • D. The SAP HANA database for model storage

Answer: A,B


NEW QUESTION # 45
What capabilities does the Exploration and Development feature of the generative Al hub provide? Note: There are 2 correct answers to this question.

  • A. Al playground and chat
  • B. Prompt editor and management
  • C. Automatic model selection
  • D. Develop and debug ABAP code

Answer: A,B


NEW QUESTION # 46
What defines SAP's approach to LLMs?

  • A. Focusing solely on reducing the computational cost of training LLMs
  • B. Ensuring ethical AI practices and seamless business integration
  • C. Limiting LLM usage to non-business applications only
  • D. Prioritizing the development of proprietary LLMs with no integration to existing systems

Answer: B

Explanation:
SAP's approach to Large Language Models (LLMs) is centered on integrating these powerful AI tools into its enterprise ecosystem while adhering to ethical standards. Unlike option A, SAP does not focus solely on proprietary LLMs without integration; instead, it leverages both proprietary and third-party models (e.g., via partnerships with providers like Azure OpenAI) to enhance business applications. Option B is incorrect because reducing computational cost is not the sole focus-SAP prioritizes value delivery through integration with business processes. Option D is also inaccurate, as SAP explicitly targets business applications rather than limiting LLMs to non-business use. Option C is correct because SAP emphasizes ethical AI practices (e.
g., through its AI Ethics Policy) and seamless integration with tools like SAP S/4HANA and SAP SuccessFactors, ensuring LLMs enhance enterprise workflows responsibly and effectively.


NEW QUESTION # 47
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 enable the matching of different relevant passages to user queries
  • C. To simplify the process of training the embedding model
  • D. To reduce the storage space required for the vector database

Answer: B

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 # 48
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SAP C-AIG-2412 Questions - C-AIG-2412 PDF Dumps [2025]

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