1Z0-1127-25試験の準備方法｜実用的な1Z0-1127-25勉強時間試験｜権威のあるOracle Cloud Infrastructure 2025 Generative AI Professional試験勉強攻略

かつてないほどの才能の才能が大量に出てきたので、現代の才能はどのような能力を所有し、最終的に成功へと歩むべきでしょうか？まあ、もちろん、それはあなたに社会での地位の資本を与える1Z0-1127-25試験資格認定です。 1Z0-1127-25準備資料では、公式の試験銀行に最新の学習モデルと包括的な知識構造が表示されます。これは、技術スキルの向上と将来への価値の創造を目的としています。 1Z0-1127-25試験の高度な質問とともに1Z0-1127-25試験に合格する必要があります。

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	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 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	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 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 (Q28-Q33):

質問 # 28
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It selectively updates only a fraction of weights to reduce the number of parameters.
B. It increases the training time as compared to Vanilla fine-tuning.
C. It updates all the weights of the model uniformly.
D. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning (a Parameter-Efficient Fine-Tuning method) updates a small subset of the model's weights, reducing computational cost and mitigating overfitting compared to Vanilla fine-tuning, which updates all weights. This makes Option C correct. Option A describes Vanilla fine-tuning, not T-Few. Option B is incomplete, as it omits the overfitting benefit. Option D is false, as T-Few typically reduces training time due to fewer updates. T-Few balances efficiency and performance.
OCI 2025 Generative AI documentation likely describes T-Few under fine-tuningoptions.

質問 # 29
What do prompt templates use for templating in language model applications?

A. Python's class and object structures
B. Python's lambda functions
C. Python's list comprehension syntax
D. Python's str.format syntax

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.

質問 # 30
What does the RAG Sequence model do in the context of generating a response?

A. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
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 modifies the input query before retrieving relevant documents to ensure a diverse response.

正解：C

解説：
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.

質問 # 31
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
B. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
C. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
D. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning updates all model parameters on task-specific data, incurring high computational costs, while PEFT (e.g., LoRA, T-Few) updates a small subset of parameters, reducing resource demands and often requiring less data, making Option A correct. Option B is false-PEFT doesn't replace architecture. Option C is incorrect, as PEFT isn't trained from scratch and is less intensive. Option D is wrong, as both involve modification, but PEFT is more efficient. This distinction is critical for practical LLM customization.
OCI 2025 Generative AI documentation likely compares Fine-tuning and PEFT under customization techniques.
Here is the next batch of 10 questions (31-40) 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 # 32
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

A. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.
B. GPUs are shared with other customers to maximize resource utilization.
C. Each customer's GPUs are connected via a public Internet network for ease of access.
D. GPUs are used exclusively for storing large datasets, not for computation.

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.
OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.

質問 # 33
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1Z0-1127-25試験の準備方法｜実用的な1Z0-1127-25勉強時間試験｜権威のあるOracle Cloud Infrastructure 2025 Generative AI Professional試験勉強攻略

Oracle 1Z0-1127-25 認定試験の出題範囲：

早速ダウンロードOracle 1Z0-1127-25勉強時間 は主要材料 & 人気のある1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional

Oracle Cloud Infrastructure 2025 Generative AI Professional 認定 1Z0-1127-25 試験問題 (Q28-Q33):

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早速ダウンロードOracle 1Z0-1127-25勉強時間は主要材料 & 人気のある1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional