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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	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 2	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 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 (Q46-Q51):

質問 # 46
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Ranker
B. Encoder-decoder
C. Retriever
D. Generator

正解：A

解説：
In Retrieval-Augmented Generation (RAG), the component responsible for evaluating and prioritizing the information retrieved by the retrieval system is the Ranker. After the Retriever fetches relevant documents or passages, the Ranker assesses these retrieved items based on their relevance to the query. It then prioritizes them, typically scoring and ordering the documents so that the most pertinent information is considered first in the generation process. This ensures that the generated response is based on the most relevant and useful content available.
Reference
Research papers on RAG (Retrieval-Augmented Generation)
Technical documentation on the architecture of RAG models

質問 # 47
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in an unencrypted form in Object Storage
B. Stored in Object Storage encrypted by default
C. Shared among multiple customers for efficiency
D. Stored in Key Management service

正解：B

質問 # 48
What is the primary purpose of LangSmith Tracing?

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

正解：B

質問 # 49
How are documents usually evaluated in the simplest form of keyword-based search?

A. Based on the presence and frequency of the user-provided keywords
B. By the complexity of language used in the documents
C. According to the length of the documents
D. Based on the number of images and videos contained in the documents

正解：A

解説：
In the simplest form of keyword-based search, documents are evaluated based on keyword matching and term frequency. This approach does not account for context, semantics, or the meaning behind the words, but rather focuses on:
Presence of Keywords - If a document contains the search term, it is considered relevant.
Term Frequency (TF) - The more a keyword appears in a document, the higher the ranking in basic search algorithms.
Inverse Document Frequency (IDF) - Words that are common across many documents (e.g., "the," "is") are given less weight, while rare words are prioritized.
Boolean Matching - Some basic search engines support logical operators like AND, OR, and NOT to refine keyword searches.
Exact Match vs. Partial Match - Some systems prioritize exact keyword matches, while others allow partial or fuzzy matches.
🔹 Oracle Generative AI Reference:
Oracle has implemented semantic search and advanced AI-driven document search techniques in its cloud solutions, but traditional keyword-based search still forms the foundation of many enterprise search mechanisms.

質問 # 50
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

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

正解：B

質問 # 51
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