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1Z0-1127-25최신업데이트덤프, 1Z0-1127-25인기시험덤프

Itexamdump의Oracle 1Z0-1127-25시험자료의 문제와 답이 실제시험의 문제와 답과 아주 비슷합니다. 우리의 짧은 학습가이드로 빠른 시일 내에 관련지식을 터득하여 응시준비를 하게 합니다. 우리는 우리의Oracle 1Z0-1127-25인증시험덤프로 시험패스를 보장합니다.

Oracle 1Z0-1127-25 시험요강:

주제
소개

주제 1

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.

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

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.

>> 1Z0-1127-25최신 업데이트 덤프 <<

1Z0-1127-25인기시험덤프 - 1Z0-1127-25최신 시험 최신 덤프

우리Itexamdump 에서는 여러분들한테 아주 편리하고 시간 절약함과 바꿀 수 있는 좋은 대책을 마련하였습니다. Itexamdump에서는Oracle 1Z0-1127-25인증시험관련가이드로 효과적으로Oracle 1Z0-1127-25시험을 패스하도록 도와드리겠습니다.만약 여러분이 다른 사이트에서도 관련덤프자료를 보셨을 경우 페이지 아래를 보시면 자료출처는 당연히 Itexamdump 일 것입니다. Itexamdump의 자료만의 제일 전면적이고 또 최신 업데이트일것입니다.

최신 Oracle Cloud Infrastructure 1Z0-1127-25 무료샘플문제 (Q83-Q88):

질문 # 83
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Generator
B. Retriever
C. Ranker
D. Encoder-Decoder

정답：C

설명：
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.

질문 # 84
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It controls the randomness of the model's output, affecting its creativity.
B. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
C. It specifies a string that tells the model to stop generating more content.
D. It determines the maximum number of tokens the model can generate per response.

정답：C

설명：
Comprehensive and Detailed In-Depth Explanation=
The "stop sequence" parameter defines a string (e.g., "." or " ") that, when generated, halts text generation, allowing control over output length or structure-Option A is correct. Option B (penalty) describes frequency/presence penalties. Option C (max tokens) is a separate parameter. Option D (randomness) relates to temperature. Stop sequences ensure precise termination.
OCI 2025 Generative AI documentation likely details stop sequences under generation parameters.

질문 # 85
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. "Top p" assigns penalties to frequently occurring tokens.
B. "Top p" determines the maximum number of tokens per response.
C. "Top p" limits token selection based on the sum of their probabilities.
D. "Top p" selects tokens from the "Top k" tokens sorted by probability.

정답：C

설명：
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.

질문 # 86
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

A. StreamlitChatMessageHistory can be used in any type of LLM application.
B. A given StreamlitChatMessageHistory will NOT be persisted.
C. A given StreamlitChatMessageHistory will not be shared across user sessions.
D. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.

정답：A

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 # 87
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Improved retrievals for Retrieval Augmented Generation (RAG) systems
B. Capacity to translate text in over 100 languages
C. Emphasis on syntactic clustering of word embeddings
D. Support for tokenizing longer sentences

정답：A

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 # 88
......

Itexamdump의 Oracle인증 1Z0-1127-25덤프는 다른 덤프판매 사이트보다 저렴한 가격으로 여러분들께 가볍게 다가갑니다. Oracle인증 1Z0-1127-25덤프는 기출문제와 예상문제로 되어있어 시험패스는 시간문제뿐입니다.

1Z0-1127-25인기시험덤프: https://www.itexamdump.com/1Z0-1127-25.html

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