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Oracle 1z0-1127-24 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.
トピック 2	Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.
トピック 3	Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

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Oracle Cloud Infrastructure 2024 Generative AI Professional 認定 1z0-1127-24 試験問題 (Q60-Q65):

質問 # 60
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

A. T- Few fine-tuning involves updating the weights of all layers in the model.
B. T-Few fine-tuning requires manual annotation of input-output pain.
C. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
D. T-Few fine-tuning relies on unsupervised learning techniques for annotation.

正解：C

解説：
T-Few fine-tuning is a technique that uses annotated data to adjust only a fraction of the model's weights. This method aims to efficiently fine-tune the model with a limited amount of data and computational resources. By updating only a small subset of the parameters, T-Few fine-tuning can achieve significant performance improvements without the need for extensive training data or computational power.
Reference
Research papers on parameter-efficient fine-tuning techniques
Technical guides on T-Few fine-tuning methodology

質問 # 61
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

正解：C

質問 # 62
Which is NOT a typical use case for LangSmith Evaluators?

A. Detecting bias or toxicity
B. Aliening code readability
C. Measuring coherence of generated text
D. Evaluating factual accuracy of outputs

正解：B

解説：
LangSmith Evaluators are not typically used for aligning code readability. Instead, they are used for tasks such as measuring the coherence of generated text, evaluating the factual accuracy of outputs, and detecting bias or toxicity. Evaluators help ensure the quality and reliability of the outputs generated by language models.
Reference
LangSmith documentation on evaluators
Research articles on evaluation metrics for language models

質問 # 63
What is the primary purpose of LangSmith Tracing?

A. To debug issues in language model outputs
B. To monitor the performance of language models
C. To generate test cases for language models
D. To analyze the reasoning process of language

正解：D

質問 # 64
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

A. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
C. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
D. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.

正解：A

解説：
In Retrieval-Augmented Generation (RAG), two key evaluation metrics are Groundedness and Answer Relevance, each addressing different aspects of AI-generated responses.
Groundedness (Factual Correctness):
Determines whether the AI-generated answer is factually based on the retrieved data.
Ensures that AI responses are verifiable from the reference documents.
Prevents hallucination, where the model generates plausible but incorrect answers.
Answer Relevance (Query Alignment):
Evaluates how well the response aligns with the user's original query.
A response may be factually correct but irrelevant to the actual question.
Ensures that AI responses remain contextually appropriate.
Key Differences:
Groundedness ensures that responses are based on correct data.
Answer Relevance checks whether the response actually answers the user's query.
🔹 Oracle Generative AI Reference:
Oracle AI ensures fact-checking and contextual accuracy in its retrieval-augmented models, reducing AI hallucination risks.

質問 # 65
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