Mike Evans Mike Evans's Página de perfil

Mike Evans Mike Evans

0 Inscritos en el curso • 0 Curso completado

Biografía

Oracle 1Z0-1127-25考試重點 - 1Z0-1127-25考試證照綜述

VCESoft提供高品質的最佳學習資料，讓通過Oracle 1Z0-1127-25考試從未如此快速、便宜、和簡單。有了最新詳細的題庫和答案，為您的1Z0-1127-25考試做好充分的準備，我們將保證您在考試中取得成功。在購買前，您還可以下載我們提供的1Z0-1127-25免費DEMO來試用，這是非常有效的學習資料。通過客戶的完全信任，我們為考生提供真實有效的訓練，幫助大家在第一次Oracle 1Z0-1127-25考試中順利通過。

Oracle 1Z0-1127-25 考試大綱：

主題
簡介

主題 1

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.

主題 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.

主題 3

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 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.

>> Oracle 1Z0-1127-25考試重點 <<

1Z0-1127-25考試證照綜述 - 1Z0-1127-25考試證照

上帝是很公平的，每個人都是不完美的。就好比我，平時不努力，老大徒傷悲。現在的IT行業競爭壓力不言而喻大家都知道，每個人都想通過IT認證來提升自身的價值，我也是，可是這種對我們來說是太難太難了，所學的專業知識早就忘了，惡補那是不現實的，還好我在互聯網上看到了VCESoft Oracle的1Z0-1127-25考試培訓資料，有了它我就不用擔心我得考試了，VCESoft Oracle的1Z0-1127-25考試培訓資料真的很好，它的內容覆蓋面廣，而且針對性強，絕對比我自己復習去準備考試好，如果你也是IT行業中的一員，那就趕緊將VCESoft Oracle的1Z0-1127-25考試培訓資料加入購物車吧，不要猶豫，不要徘徊，VCESoft Oracle的1Z0-1127-25考試培訓資料絕對是成功最好的伴侶。

最新的 Oracle Cloud Infrastructure 1Z0-1127-25 免費考試真題 (Q13-Q18):

問題 #13
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?

A. Least-to-Most Prompting
B. Chain-of-Thought
C. Step-Back Prompting
D. In-context Learning

答案：B

解題說明：
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn't focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.
OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.

問題 #14
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?

A. Semantic relationships; crucial for understanding context and generating precise language
B. Linear relationships; they simplify the modeling process
C. Temporal relationships; necessary for predicting future linguistic trends
D. Hierarchical relationships; important for structuring database queries

答案：A

解題說明：
Comprehensive and Detailed In-Depth Explanation=
Vector databases store embeddings that preserve semantic relationships (e.g., similarity between "dog" and "puppy") via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn't the focus-semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.
OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.

問題 #15
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Shared among multiple customers for efficiency
B. Stored in an unencrypted form in Object Storage
C. Stored in Key Management service
D. Stored in Object Storage encrypted by default

答案：D

解題說明：
Comprehensive and Detailed In-Depth Explanation=
In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices-Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.
OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.

問題 #16
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

A. 20 unit hours
B. 240 unit hours
C. 480 unit hours
D. 744 unit hours

答案：B

解題說明：
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.

問題 #17
What is LangChain?

A. A Java library for text summarization
B. A Ruby library for text generation
C. A Python library for building applications with Large Language Models
D. A JavaScript library for natural language processing

答案：C

解題說明：
Comprehensive and Detailed In-Depth Explanation=
LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone-it's broader in scope. It's widely used for LLM-powered apps.
OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.

問題 #18
......

我相信不論在哪個行業工作的人都希望自己有很好的職業前景。當然在競爭激烈的IT行業裏面也不例外。在IT行業中工作的專業人士也希望自己有個很好的提升機會和很大的提升空間。很多專業的IT人士都知道Oracle 1Z0-1127-25 認證考試可以幫你滿足這些願望的。而VCESoft是一個能幫助你成功通過Oracle 1Z0-1127-25 的網站。

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