完璧1Z0-1127-25｜100％合格率の1Z0-1127-25関連資格知識試験｜試験の準備方法Oracle Cloud Infrastructure 2025 Generative AI Professional赤本勉強

ほとんどの人は勉強中にコンピューターを使用することを好むかもしれませんが、Oracleコンピューターで勉強することは目に害を及ぼすと考えているため、多くの人が紙の購入を学びたいと認めている必要があります。PassTest 1Z0-1127-25テスト問題には、顧客のニーズを満たすために印刷をサポートする機能があります。正常にダウンロードしたら、1Z0-1127-25試験問題をOracle Cloud Infrastructure 2025 Generative AI Professional論文に印刷できます。目を保護するだけでなく、メモをとるのに非常に便利です。 1Z0-1127-25試験準備を気に入っていただけると信じています。

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	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 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.
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.

>> 1Z0-1127-25関連資格知識 <<

1Z0-1127-25赤本勉強、1Z0-1127-25テスト問題集

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Oracle Cloud Infrastructure 2025 Generative AI Professional 認定 1Z0-1127-25 試験問題 (Q13-Q18):

質問 # 13
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?

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

正解：D

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 # 14
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

A. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
B. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
C. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
D. Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague-both measure similarity, not distinct content vs. topicality. Option B is false-both address semantics, not syntax. Option D is incorrect-neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.
OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

質問 # 15
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

正解：C

解説：
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.

質問 # 16
Given the following code:
chain = prompt | llm
Which statement is true about LangChain Expression Language (LCEL)?

A. LCEL is an older Python library for building Large Language Models.
B. LCEL is a programming language used to write documentation for LangChain.
C. LCEL is a declarative and preferred way to compose chains together.
D. LCEL is a legacy method for creating chains in LangChain.

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation=
LangChain Expression Language (LCEL) is a declarative syntax (e.g., using | to pipe components) for composing chains in LangChain, combining prompts, LLMs, and other elements efficiently-Option C is correct. Option A is false-LCEL isn't for documentation. Option B is incorrect-it's current, not legacy; traditional Python classes are older. Option D is wrong-LCEL is part of LangChain, not a standalone LLM library. LCEL simplifies chain design.
OCI 2025 Generative AI documentation likely highlights LCEL under LangChain chaincomposition.

質問 # 17
In which scenario is soft prompting especially appropriate compared to other training styles?

A. When the model needs to be adapted to perform well in a different domain it was not originally trained on.
B. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
C. When the model requires continued pre-training on unlabeled data.
D. When there is a significant amount of labeled, task-specific data available.

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.
OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.

質問 # 18
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