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>> 1z0-1127-24科目対策 <<

1z0-1127-24トレーニング、1z0-1127-24対策学習

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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.

Oracle Cloud Infrastructure 2024 Generative AI Professional 認定 1z0-1127-24 試験問題 (Q18-Q23):

質問 # 18
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 shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
D. It transforms their architecture from a neural network to a traditional database system.

正解：C

質問 # 19
What is the primary purpose of LangSmith Tracing?

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

正解：A

質問 # 20
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

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

正解：C

解説：
The Cohere Embed v3 model distinguishes itself from its predecessor in the OCI Generative AI service primarily through improved retrievals for Retrieval Augmented Generation (RAG) systems. This enhancement means that the new version of the model is better at retrieving relevant documents or passages that can be used to augment the generation of responses. The improvements likely include better embedding quality, which allows the model to find more relevant and contextually appropriate information during the retrieval phase.
Reference
Cohere model documentation and release notes
Technical discussions on improvements in RAG systems

質問 # 21
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
B. It com rob the randomness of the model* output, affecting its creativity.
C. It determines the maximum number of tokens the model can generate per response.
D. It specifies a string that tells the model to stop generating more content

正解：D

解説：
The "stop sequence" parameter in the OCI Generative AI Generation models is used to specify a string that signals the model to stop generating further content. When the model encounters this string during the generation process, it terminates the response. This parameter is useful for controlling the length and content of the generated text, ensuring that the output meets specific requirements or constraints.
Reference
OCI Generative AI service documentation
General principles of sequence generation in AI models

質問 # 22
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

A. Vector Stores
B. LangChain Application
C. LLMs
D. Document Loaders

正解：C

解説：
LangChain is an open-source framework that helps integrate Large Language Models (LLMs) into applications. In a chatbot system, the LLM (Large Language Model) component is responsible for generating linguistic output, as it processes user inputs and generates human-like responses.
Key components of LangChain include:
Document Loaders - Responsible for extracting and processing external data sources before passing them to the LLM.
Vector Stores - Used for storing and retrieving vector embeddings of documents for semantic search and similarity retrieval.
LLMs (Large Language Models) - This is the core component responsible for understanding prompts and generating text-based outputs in a chatbot.
LangChain Applications - The overall framework that connects all components but does not directly generate text.
🔹 Oracle Generative AI Reference:
Oracle supports LLM-driven chatbots and enterprise AI solutions, utilizing frameworks like LangChain to enhance AI capabilities.

質問 # 23
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