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Gabe Lee Gabe Lee

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C-AIG-2412인증시험대비공부자료, C-AIG-2412인기자격증덤프공부자료

IT인증시험에 도전해보려는 분들은 회사에 다니는 분들이 대부분입니다. 승진을 위해서나 연봉협상을 위해서나 자격증 취득은 지금시대의 필수입니다. DumpTOP의SAP인증 C-AIG-2412덤프는 회사다니느라 바쁜 나날을 보내고 있는 분들을 위해 준비한 시험준비공부자료입니다. DumpTOP의SAP인증 C-AIG-2412덤프를 구매하여 pdf버전을 공부하고 소프트웨어버전으로 시험환경을 익혀 시험보는게 두렵지 않게 해드립니다. 문제가 적고 가격이 저렴해 누구나 부담없이 애용 가능합니다. DumpTOP의SAP인증 C-AIG-2412덤프를 데려가 주시면 기적을 안겨드릴게요.

DumpTOP SAP C-AIG-2412덤프의 질문들과 답변들은 100%의 지식 요점과 적어도 98%의SAP C-AIG-2412시험 문제들을 커버하는 수년동안 가장 최근의SAP C-AIG-2412 시험 요점들을 컨설팅 해 온 시니어 프로 IT 전문가들의 그룹에 의해 구축 됩니다. SAP C-AIG-2412 시험적중율 높은 덤프로 시험패스하세요.

>> C-AIG-2412인증시험대비 공부자료 <<

C-AIG-2412인기자격증 덤프공부자료, C-AIG-2412유효한 공부자료

최근 IT 업종에 종사하는 분들이 점점 늘어가는 추세하에 경쟁이 점점 치열해지고 있습니다. IT인증시험은 국제에서 인정받는 효력있는 자격증을 취득하는 과정으로서 널리 알려져 있습니다. DumpTOP의 SAP인증 C-AIG-2412덤프는IT인증시험의 한 과목인 SAP인증 C-AIG-2412시험에 대비하여 만들어진 시험전 공부자료인데 높은 시험적중율과 친근한 가격으로 많은 사랑을 받고 있습니다.

최신 SAP Certified Associate C-AIG-2412 무료샘플문제 (Q58-Q63):

질문 # 58
What is a significant risk associated with using LLMs?

A. Potential biases in generated content
B. Complete elimination of human oversight in content creation
C. Unlimited processing power usage without cost control
D. Inability to generate text in multiple languages

정답：A

설명：
A significant risk of using LLMs is the potential for biases in generated content, stemming from biases present in their training data. Option A is incorrect because LLMs do not inherently eliminate human oversight; oversight is often maintained, especially in enterprise settings like SAP's. Option B is false as LLMs can generate text in multiple languages, as seen with models like GPT-4. Option D, while a concern in terms of resource management, is not the most significant risk compared to bias, and cost control can be implemented. Option C is correct because biased outputs can lead to unfair decisions or misinformation, a risk SAP mitigates through its AI Ethics framework, which includes principles like avoiding bias and discrimination, ensuring responsible AI deployment.

질문 # 59
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

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

정답：C

설명：
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.

질문 # 60
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 embedding model stores the generated ans wers for future reference.
C. The retrieved content is combined with the LLM's capabilities to generate a response.
D. The user's questi on is used to search a knowledge base or a set of documents.

정답：C,D

설명：
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.

질문 # 61
Which of the following is a benefit of using Retrieval Augmented Generation?

A. It allows LLMs to access and utilize information beyond their initial training data.
B. It reduces the computational resources required for language modeling.
C. It enables LLMs to learn new languages without additional training.
D. It eliminates the need for fine-tuning LLMs for specific tasks.

정답：A

질문 # 62
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.

A. To interpret human instructions and control software systems without necessarily producing output for human consumption.
B. To interpret human instructions and control software systems always producing output for human consumption.
C. To follow a specific schema - human input, Al processing, and output for human consumption.
D. To produce outputs based on software input.

정답：A,B

설명：
* C. To interpret human instructions and control software systems without necessarily producing output for human consumption.This is a key area where generative AI is breaking new ground. Think of it as AI acting as a "middleman" between you and software. Here are some examples:
* Automating complex tasks:You could tell the AI to "optimize this database for performance" or
"find and fix security vulnerabilities in this code." The AI would then interact with the software systems to carry out these instructions, without needing to show you every step or result.
* Controlling robots or IoT devices:Imagine instructing an AI to "adjust the lighting in the meeting room" or "have the robot retrieve the package from the warehouse." The AI translates your instructions into actions for those systems.
* Managing cloud resources:AI could dynamically allocate cloud resources based on your needs, scaling them up or down without your direct intervention.
* D. To interpret human instructions and control software systems always producing output for human consumption.This is more in line with traditional chatbot interactions, but with a broader scope. It's about AI generating outputs that are directly useful or informative for humans. Examples include:
* Creating realistic images or videos:Based on your description, the AI could generate a photorealistic image of a new product design or a short video clip for a marketing campaign.
* Writing different kinds of creative text formats:AI can generate stories, poems,articles, summaries, and even code, all tailored to your specifications.
* Providing personalized recommendations:AI can analyze your preferences and provide recommendations for products, services, or information.
Why the other options are incorrect:
* A. To produce outputs based on software input.This is a general capability of AI, not something specific to generative AI or beyond chatbots. Many AI systems analyze software input (like sensor data or log files) to produce outputs.
* B. To follow a specific schema - human input, AI processing, and output for human consumption.
This describes the basic interaction pattern of many AI systems, including chatbots. It's not something that specifically differentiates generative AI or goes beyond typical chatbot applications.

질문 # 63
......

SAP 인증 C-AIG-2412시험에 도전해보려고 결정하셨다면 DumpTOP덤프공부가이드를추천해드립니다. DumpTOP덤프는 고객님께서 필요한것이 무엇인지 너무나도 잘 알고 있답니다. DumpTOP의 SAP 인증 C-AIG-2412덤프는SAP 인증 C-AIG-2412시험을 쉽게 만듭니다.

C-AIG-2412인기자격증 덤프공부자료: https://www.dumptop.com/SAP/C-AIG-2412-dump.html

SAP C-AIG-2412인증시험대비 공부자료 덤프에 있는 문제와 답만 달달 외우시면 자격증시험이라는 높은 벽을 순식간에 무너뜨립니다, DumpTOP의 경험이 풍부한 전문가들이SAP C-AIG-2412인증시험관련자료들을 계획적으로 페펙트하게 만들었습니다.SAP C-AIG-2412인증시험응시에는 딱 좋은 자료들입니다, SAP 인증 C-AIG-2412덤프자료는DumpTOP의 전문가들이 최선을 다하여 갈고닦은 예술품과도 같습니다.100% 시험에서 패스하도록 저희는 항상 힘쓰고 있습니다, DumpTOP의 IT전문가들이 자신만의 경험과 끊임없는 노력으로 최고의SAP C-AIG-2412학습자료를 작성해 여러분들이SAP C-AIG-2412시험에서 패스하도록 도와드립니다.

얼굴이 붉으락푸르락하던 루드비히는 저택에 들어와 처음으로 카시스를C-AIG-2412향해 언성을 높였다, 에로스는 죽었어, 덤프에 있는 문제와 답만 달달 외우시면 자격증시험이라는 높은 벽을 순식간에 무너뜨립니다, DumpTOP의 경험이 풍부한 전문가들이SAP C-AIG-2412인증시험관련자료들을 계획적으로 페펙트하게 만들었습니다.SAP C-AIG-2412인증시험응시에는 딱 좋은 자료들입니다.

퍼펙트한 C-AIG-2412인증시험대비 공부자료 최신버전 자료

SAP 인증 C-AIG-2412덤프자료는DumpTOP의 전문가들이 최선을 다하여 갈고닦은 예술품과도 같습니다.100% 시험에서 패스하도록 저희는 항상 힘쓰고 있습니다, DumpTOP의 IT전문가들이 자신만의 경험과 끊임없는 노력으로 최고의SAP C-AIG-2412학습자료를 작성해 여러분들이SAP C-AIG-2412시험에서 패스하도록 도와드립니다.

SAP C-AIG-2412시험패스는 어려운 일이 아닙니다.

Gabe Lee Gabe Lee

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