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1Z0-1127-25試験問題集、Oracle 1Z0-1127-25資料は大好評を博します

PassTestの Oracleの1Z0-1127-25試験トレーニング資料はPassTestの実力と豊富な経験を持っているIT専門家が研究したもので、本物のOracleの1Z0-1127-25試験問題とほぼ同じです。それを利用したら、君のOracleの1Z0-1127-25認定試験に合格するのは問題ありません。もしPassTestの学習教材を購入した後、どんな問題があれば、或いは試験に不合格になる場合は、私たちが全額返金することを保証いたします。PassTestを信じて、私たちは君のそばにいるから。

Oracle 1Z0-1127-25 認定試験の出題範囲：

トピック
出題範囲

トピック 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.

トピック 2

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.

トピック 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.

トピック 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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional 認定 1Z0-1127-25 試験問題 (Q45-Q50):

質問 # 45
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" limits token selection based on the sum of their probabilities.
C. "Top p" determines the maximum number of tokens per response.
D. "Top p" selects tokens from the "Top k" tokens sorted by probability.

正解：B

解説：
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.

質問 # 46
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?

A. It transforms their architecture from a neural network to a traditional database system.
B. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
C. It limits their ability to understand and generate natural language.
D. It enables them to bypass the need for pretraining on large text corpora.

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
RAG integrates vector databases to retrieve real-time external data, augmenting the LLM's pretrained knowledge with current, specific information, shifting response generation to a hybrid approach-Option B is correct. Option A is false-architecture remains neural; only data sourcing changes. Option C is incorrect-pretraining is still required; RAG enhances it. Option D is wrong-RAG improves, not limits, generation. This shift enables more accurate, up-to-date responses.
OCI 2025 Generative AI documentation likely details RAG's impact under responsegeneration enhancements.

質問 # 47
Why is normalization of vectors important before indexing in a hybrid search system?

A. It significantly reduces the size of the database.
B. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
C. It ensures that all vectors represent keywords only.
D. It converts all sparse vectors to dense vectors.

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.

質問 # 48
Which is NOT a built-in memory type in LangChain?

A. ConversationTokenBufferMemory
B. ConversationSummaryMemory
C. ConversationImageMemory
D. ConversationBufferMemory

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation=
LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)-Options B, C, and D are valid. ConversationImageMemory (A) isn't a standard type-image handling typically requires custom or multimodal extensions, not a built-in memory class-making A correct as NOT included.
OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.

質問 # 49
An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?

A. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.
B. An LLM dedicated to generating text responses without external data integration.
C. A keyword search-based AI that responds based on specific keywords identified in customer queries.
D. A pre-trained LLM model from Cohere or OpenAI.

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can't dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG's capabilities. RAG meets both requirements effectively.
OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

質問 # 50
......

PassTestはたくさんOracle関連1Z0-1127-25認定試験の受験者に利便性を提供して、多くの人がPassTestの問題集を使うので試験に合格しますた。彼らはPassTestの問題集が有効なこと確認しました。PassTestが提供しておりますのは専門家チームの研究した1Z0-1127-25問題と真題で弊社の高い名誉はたぶり信頼をうけられます。安心で弊社の商品を使うために無料なサンブルをダウンロードしてください。

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