最新の2026年03月13日 1Z0-1127-25問題集は学習ガイドは試験合格するための秘訣
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Oracle 1Z0-1127-25 認定試験の出題範囲：
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質問 # 45
How are prompt templates typically designed for language models?
- A. As complex algorithms that require manual compilation
- B. To be used without any modification or customization
- C. As predefined recipes that guide the generation of language model prompts
- D. To work only with numerical data instead of textual content
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren't complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.
Here is the next batch of 10 questions (21-30) 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 # 46
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?
- A. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
- B. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
- C. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
- D. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
正解：A
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 # 47
What is the purpose of Retrievers in LangChain?
- A. To retrieve relevant information from knowledge bases
- B. To break down complex tasks into smaller steps
- C. To combine multiple components into a single pipeline
- D. To train Large Language Models
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation=
Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated-Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.
OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.
質問 # 48
What is the purpose of embeddings in natural language processing?
- A. To translate text into a different language
- B. To increase the complexity and size of text data
- C. To compress text data into smaller files for storage
- D. To create numerical representations of text that capture the meaning and relationships between words or phrases
正解：D
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 # 49
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Translation models
- B. Embedding models
- C. Summarization models
- D. Generation models
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.
質問 # 50
What is the purpose of frequency penalties in language model outputs?
- A. To randomly penalize some tokens to increase the diversity of the text
- B. To penalize tokens that have already appeared, based on the number of times they have been used
- C. To reward the tokens that have never appeared in the text
- D. To ensure that tokens that appear frequently are used more often
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
Frequency penalties reduce the likelihood of repeating tokens that have already appeared in the output, based on their frequency, to enhance diversity and avoid repetition. This makes Option B correct. Option A is the opposite effect. Option C describes a different mechanism (e.g., presence penalty in some contexts). Option D is inaccurate, as penalties aren't random but frequency-based.
OCI 2025 Generative AI documentation likely covers frequency penalties under output control parameters.
Below is the next batch of 10 questions (11-20) from your list, formatted as requested with detailed explanations. These 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 # 51
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?
- A. When the LLM does not perform well on a task and the data for prompt engineering is too large
- B. When the LLM requires access to the latest data for generating outputs
- C. When you want to optimize the model without any instructions
- D. When the LLM already understands the topics necessary for text generation
正解：A
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
What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?
- A. A technique used to enhance the model's performance on specific tasks
- B. The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
- C. The process by which the model visualizes and describes images in detail
- D. The model's ability to generate imaginative and creative content
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
In LLMs, "hallucination" refers to the generation of plausible-sounding but factually incorrect or irrelevant content, often presented with confidence. This occurs due to the model's reliance on patterns in training data rather than factual grounding, making Option D correct. Option A describes a positive trait, not hallucination. Option B is unrelated, as hallucination isn't a performance-enhancing technique. Option C pertains to multimodal models, not the general definition of hallucination in LLMs.
OCI 2025 Generative AI documentation likely addresses hallucination under model limitations or evaluation metrics.
質問 # 53
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
- A. By allowing updates across all layers of the model
- B. By incorporating additional layers to the base model
- C. By restricting updates to only a specific group of transformer layers
- D. By excluding transformer layers from the fine-tuning process entirely
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.
質問 # 54
What is LCEL in the context of LangChain Chains?
- A. A legacy method for creating chains in LangChain
- B. A programming language used to write documentation for LangChain
- C. An older Python library for building Large Language Models
- D. A declarative way to compose chains together using LangChain Expression Language
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains-sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.
質問 # 55
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?
- A. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
- B. A given StreamlitChatMessageHistory will NOT be persisted.
- C. StreamlitChatMessageHistory can be used in any type of LLM application.
- D. A given StreamlitChatMessageHistory will not be shared across user sessions.
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
StreamlitChatMessageHistory integrates with Streamlit's session state to store chat history, tied to a specific key (Option A, true). It's not persisted beyond the session (Option B, true) and isn't shared across users (Option C, true), as Streamlit sessions are user-specific. However, it's designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true.
OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.
質問 # 56
When does a chain typically interact with memory in a run within the LangChain framework?
- A. Only after the output has been generated.
- B. Before user input and after chain execution.
- C. Continuously throughout the entire chain execution process.
- D. After user input but before chain execution, and again after core logic but before output.
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.
質問 # 57
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?
- A. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
- B. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
- C. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
- D. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning updates all model parameters on task-specific data, incurring high computational costs, while PEFT (e.g., LoRA, T-Few) updates a small subset of parameters, reducing resource demands and often requiring less data, making Option A correct. Option B is false-PEFT doesn't replace architecture. Option C is incorrect, as PEFT isn't trained from scratch and is less intensive. Option D is wrong, as both involve modification, but PEFT is more efficient. This distinction is critical for practical LLM customization.
OCI 2025 Generative AI documentation likely compares Fine-tuning and PEFT under customization techniques.
Here is the next batch of 10 questions (31-40) 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 # 58
What does a cosine distance of 0 indicate about the relationship between two embeddings?
- A. They are unrelated
- B. They are similar in direction
- C. They are completely dissimilar
- D. They have the same magnitude
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
Cosine distance measures the angle between two vectors, where 0 means the vectors point in the same direction (cosine similarity = 1), indicating high similarity in embeddings' semantic content-Option C is correct. Option A (dissimilar) aligns with a distance of 1. Option B is vague-directional similarity matters. Option D (magnitude) isn't relevant-cosine ignores magnitude. This is key for semantic comparison.
OCI 2025 Generative AI documentation likely explains cosine distance under vector database metrics.
質問 # 59
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?
- A. To generate text using extra information obtained from an external data source
- B. To retrieve text from an external source and present it without any modifications
- C. To generate text based only on the model's internal knowledge without external data
- D. To store text in an external database without using it for generation
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation=
RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose-data is used, not just stored. Option D is incorrect-RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.
OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.
質問 # 60
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