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ユニークな1Z0-1127-25資格トレーリング &合格スムーズ1Z0-1127-25出題範囲 |検証する1Z0-1127-25試験解説問題

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1Z0-1127-25出題範囲、1Z0-1127-25試験解説問題

ずっと自分自身を向上させたいあなたは、1Z0-1127-25認定試験を受験する予定があるのですか。もし受験したいなら、試験の準備をどのようにするつもりですか。もしかして、自分に相応しい試験参考書を見つけたのでしょうか。では、どんな参考書は選べる価値を持っていますか。あなたが選んだのは、Japancertの1Z0-1127-25問題集ですか。もしそうだったら、もう試験に合格できないなどのことを心配する必要がないのです。

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 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.

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

• 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 Cloud Infrastructure 2025 Generative AI Professional 認定 1Z0-1127-25 試験問題 (Q56-Q61):

質問 # 56
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

• A. "Top p" selects tokens from the "Top k" tokens sorted by probability.
• B. "Top p" assigns penalties to frequently occurring tokens.
• C. "Top p" limits token selection based on the sum of their probabilities.
• D. "Top p" determines the maximum number of tokens per response.

正解：C

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 # 57
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?

• A. PromptTemplate supports any number of variables, including the possibility of having none.
• B. PromptTemplate can support only a single variable at a time.
• C. PromptTemplate is unable to use any variables.
• D. PromptTemplate requires a minimum of two variables to function properly.

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design-Option C is correct. The example shows two, but it's not a requirement. Option A (minimum two) is false-no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose-variables are optional but supported. This adaptability aids prompt engineering.
OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.

 

質問 # 58
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

• A. Capacity to translate text in over 100 languages
• B. Emphasis on syntactic clustering of word embeddings
• C. Support for tokenizing longer sentences
• D. Improved retrievals for Retrieval Augmented Generation (RAG) systems

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.
OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.

 

質問 # 59
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 # 60
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

正解：B

解説：
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.

 

質問 # 61
......

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