최신업데이트버전1Z0-1127-25최신인증시험공부자료덤프문제공부 참고: Itcertkr에서 Google Drive로 공유하는 무료 2025 Oracle 1Z0-1127-25 시험 문제집이 있습니다: https://drive.google.com/open?id=1OZoCN8ku2NPocly4DlgV9Sn2whqM-C4N
인재도 많고 경쟁도 치열한 이 사회에서 IT업계 인재들은 인기가 아주 많습니다.하지만 팽팽한 경쟁률도 무시할 수 없습니다.많은 IT인재들도 어려운 인증시험을 패스하여 자기만의 자리를 지켜야만 합니다.우리 Itcertkr에서는 마침 전문적으로 이러한 IT인사들에게 편리하게 시험을 패스할수 있도록 유용한 자료들을 제공하고 있습니다. Oracle 인증1Z0-1127-25인증은 아주 중요한 인증시험중의 하나입니다. Itcertkr의Oracle 인증1Z0-1127-25로 시험을 한방에 정복하세요.
Oracle 1Z0-1127-25 시험요강: 주제 소개 주제 1 Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.
주제 2 Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.
주제 3 Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI's security architecture for generative AI and emphasizes responsible AI practices.
주제 4 Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.
1Z0-1127-25최신 인증시험 공부자료 <<
1Z0-1127-25시험대비 최신 덤프공부자료 – 1Z0-1127-25최신버전 시험덤프 Itcertkr의 제품을 구매하시면 우리는 일년무료업데이트 서비스를 제공함으로 여러분을 인증시험을 패스하게 도와줍니다. 만약 인증시험내용이 변경이 되면 우리는 바로 여러분들에게 알려드립니다.그리고 최신버전이 있다면 바로 여러분들한테 보내드립니다. Itcertkr는 한번에Oracle 1Z0-1127-25인증시험을 패스를 보장합니다.
최신 Oracle Cloud Infrastructure 1Z0-1127-25 무료샘플문제 (Q44-Q49): 질문 # 44 Which is a key characteristic of the annotation process used in T-Few fine-tuning?
A. T-Few fine-tuning requires manual annotation of input-output pairs. B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights. C. T-Few fine-tuning relies on unsupervised learning techniques for annotation. D. T-Few fine-tuning involves updating the weights of all layers in the model. 정답：B
설명： Comprehensive and Detailed In-Depth Explanation= T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates. OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.
질문 # 45 What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?
A. Support for tokenizing longer sentences B. Capacity to translate text in over 100 languages C. Improved retrievals for Retrieval Augmented Generation (RAG) systems D. Emphasis on syntactic clustering of word embeddings 정답：C
설명： Comprehensive and Detailed In-Depth Explanation= Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness. OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.
질문 # 46 Given the following code block: history = StreamlitChatMessageHistory(key=“chatmessages”) memory = ConversationBufferMemory(chatmemory=history) Which statement is NOT true about StreamlitChatMessageHistory?
A. A given StreamlitChatMessageHistory will not be shared across user sessions. B. A given StreamlitChatMessageHistory will NOT be persisted. C. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key. D. StreamlitChatMessageHistory can be used in any type of LLM application. 정답：D
설명： Comprehensive and Detailed In-Depth Explanation= StreamlitChatMessageHistory integrates with Streamlit's session state to store chat history, tied to a specific key (Option A, true). It's not persisted beyond the session (Option B, true) and isn't shared across users (Option C, true), as Streamlit sessions are user-specific. However, it's designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true. OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.
질문 # 47 How does the structure of vector databases differ from traditional relational databases?
A. It is based on distances and similarities in a vector space. B. A vector database stores data in a linear or tabular format. C. It uses simple row-based data storage. D. It is not optimized for high-dimensional spaces. 정답：A
설명： Comprehensive and Detailed In-Depth Explanation= Vector databases store data as high-dimensional vectors, optimized for similarity searches (e.g., cosine distance), unlike relational databases' tabular, row-column structure. This makes Option C correct. Option A and D describe relational databases. Option B is false-vector databases excel in high-dimensional spaces. Vector databases support semantic queries critical for LLMs. OCI 2025 Generative AI documentation likely contrasts these under data storage options.
질문 # 48 What is the primary function of the “temperature” parameter in the OCI Generative AI Generation models?
A. Assigns a penalty to tokens that have already appeared in the preceding text B. Specifies a string that tells the model to stop generating more content C. Controls the randomness of the model's output, affecting its creativity D. Determines the maximum number of tokens the model can generate per response 정답：C
설명： Comprehensive and Detailed In-Depth Explanation= The “temperature” parameter adjusts the randomness of an LLM's output by scaling the softmax distribution-low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity-Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style. OCI 2025 Generative AI documentation likely defines temperature under generation parameters.
질문 # 49 ......
많은 사이트에서도 무료Oracle 1Z0-1127-25덤프데모를 제공합니다.우리도 마찬가지입니다.여러분은 그러한Oracle 1Z0-1127-25데모들을 보시고 다시 우리의 덤프와 비교하시면 ,우리의 덤프는 다른 사이트덤프와 차원이 다른 덤프임을 아시될것입니다, 우리Itcertkr에서 제공되는 덤프는 100%보장 도를 자랑하며,여러분은 시험패스로 인해 성공과 더 가까워 졌답니다
1Z0-1127-25시험대비 최신 덤프공부자료: https://www.itcertkr.com/1Z0-1127-25_exam.html