Zoe Reed Zoe Reed's Profile Page

Zoe Reed Zoe Reed

0 Course Enrolled • 0 Course Completed

Biography

最新1Z0-1127-25題庫資源 - 1Z0-1127-25考試證照綜述

KaoGuTi是唯一能供給你們需求的全部的Oracle 1Z0-1127-25 認證考試相關資料的網站。利用KaoGuTi提供的資料通過Oracle 1Z0-1127-25 認證考試是不成問題的，而且你可以以很高的分數通過考試得到相關認證。

Oracle 1Z0-1127-25 考試大綱：

主題
簡介

主題 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.

主題 2

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.

主題 3

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.

主題 4

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.

>> 最新1Z0-1127-25題庫資源 <<

在KaoGuTi中選擇最新1Z0-1127-25題庫資源可以輕松放心通過Oracle Cloud Infrastructure 2025 Generative AI Professional考試

我們KaoGuTi的IT認證考題擁有多年的培訓經驗，KaoGuTi Oracle的1Z0-1127-25考試培訓資料是個值得信賴的產品，我們的IT精英團隊不斷為廣大考生提供最新版的1Z0-1127-25考試培訓資料，我們的工作人員作出了巨大努力，以確保你們在考試中總是取得好成績，可以肯定的是，KaoGuTi Oracle的1Z0-1127-25考試材料是為你提供最實際的IT認證材料。

最新的 Oracle Cloud Infrastructure 1Z0-1127-25 免費考試真題 (Q28-Q33):

問題 #28
What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

A. It allows the LLM to access a larger dataset.
B. It significantly reduces the latency for each model request.
C. It provides examples in the prompt to guide the LLM to better performance with no training cost.
D. It eliminates the need for any training or computational resources.

答案：C

解題說明：
Comprehensive and Detailed In-Depth Explanation=
Few-shot prompting involves providing a few examples in the prompt to guide the LLM's behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn't expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn't significantly affected by few-shot prompting.
OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.

問題 #29
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Determines the maximum number of tokens the model can generate per response
B. Controls the randomness of the model's output, affecting its creativity
C. Assigns a penalty to tokens that have already appeared in the preceding text
D. Specifies a string that tells the model to stop generating more content

答案：B

解題說明：
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.

問題 #30
When does a chain typically interact with memory in a run within the LangChain framework?

A. After user input but before chain execution, and again after core logic but before output
B. Before user input and after chain execution
C. Continuously throughout the entire chain execution process
D. Only after the output has been generated

答案：A

解題說明：
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to retrieve context) but before execution (to inform processing), and again after core logic (to update memory) but before output (to maintain state). This makes Option C correct. Option A misses pre-execution context. Option B misplaces timing. Option D overstates-interaction is at specific stages, not continuous. Memory ensures context-aware responses.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain chain execution.

問題 #31
How are chains traditionally created in LangChain?

A. By using machine learning algorithms
B. Declaratively, with no coding required
C. Using Python classes, such as LLMChain and others
D. Exclusively through third-party software integrations

答案：C

解題說明：
Comprehensive and Detailed In-Depth Explanation=
Traditionally, LangChain chains (e.g., LLMChain) are created using Python classes that define sequences of operations, such as calling an LLM or processing data. This programmatic approach predates LCEL's declarative style, making Option C correct. Option A is vague and incorrect, as chains aren't ML algorithms themselves. Option B describes LCEL, not traditional methods. Option D is false, as third-party integrations aren't required. Python classes provide structured chain building.
OCI 2025 Generative AI documentation likely contrasts traditional chains with LCEL under LangChain sections.

問題 #32
What does the RAG Sequence model do in the context of generating a response?

A. It modifies the input query before retrieving relevant documents to ensure a diverse response.
B. It retrieves relevant documents only for the initial part of the query and ignores the rest.
C. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
D. It retrieves a single relevant document for the entire input query and generates a response based on that alone.

答案：C

解題說明：
Comprehensive and Detailed In-Depth Explanation=
The RAG (Retrieval-Augmented Generation) Sequence model retrieves a set of relevant documents for a query from an external knowledge base (e.g., via a vector database) and uses them collectively with the LLM to generate a cohesive, informed response. This leverages multiple sources for better context, making Option B correct. Option A describes a simpler approach (e.g., RAG Token), not Sequence. Option C is incorrect-RAG considers the full query. Option D is false-query modification isn't standard in RAG Sequence. This method enhances response quality with diverse inputs.
OCI 2025 Generative AI documentation likely details RAG Sequence under retrieval-augmented techniques.

問題 #33
......

為了永遠給你提供最好的1Z0-1127-25認證考試的考古題，KaoGuTi一直在不斷提高考古題的品質，並且隨時根據最新的1Z0-1127-25考試大綱更新考古題。在現在的市場上，KaoGuTi是你最好的選擇。長時間以來，KaoGuTi已經得到了眾多考生的認可。如果你不相信的話，你可以向你身邊的人打聽一下，肯定有人曾經使用過KaoGuTi的資料。我們保證給你提供最優秀的參考資料讓你一次通過考試。

1Z0-1127-25考試證照綜述: https://www.kaoguti.com/1Z0-1127-25_exam-pdf.html