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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
  • 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 2
  • 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 3
  • 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 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.

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Oracle 1Z0-1127-25 PDF問題サンプル、1Z0-1127-25試験勉強攻略

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Oracle Cloud Infrastructure 2025 Generative AI Professional 認定 1Z0-1127-25 試験問題 (Q47-Q52):

質問 # 47
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn't task-specific. Option B is incorrect as PEFT and Soft Prompting don't modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn't.
OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.


質問 # 48
What does accuracy measure in the context of fine-tuning results for a generative model?

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance-Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse-error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.


質問 # 49
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.


質問 # 50
What is the purpose of embeddings in natural language processing?

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation=
Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings' primary purpose. Option D is incorrect, as embeddings aren't primarily for compression but for representation.
OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.


質問 # 51
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.


質問 # 52
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