[2026年02月25日] 合格させる1Z0-1127-25レビューガイド、信頼され続ける1Z0-1127-25テストエンジン [Q37-Q55]

[2026年02月25日] 合格させる1Z0-1127-25レビューガイド、信頼され続ける1Z0-1127-25テストエンジン

1Z0-1127-25テストエンジン練習テスト問題、試験問題集

質問 # 37
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

• A. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
• B. PEFT modifies all parameters and is typically used when no training data exists.
• C. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
• D. PEFT involves only a few or new parameters and uses labeled, task-specific data.

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.

質問 # 38
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

• A. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
• B. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
• C. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
• D. 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.

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 # 39
What do prompt templates use for templating in language model applications?

• A. Python's str.format syntax
• B. Python's class and object structures
• C. Python's lambda functions
• D. Python's list comprehension syntax

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.

質問 # 40
When does a chain typically interact with memory in a run within the LangChain framework?

• A. Before user input and after chain execution.
• B. After user input but before chain execution, and again after core logic but before output.
• C. Continuously throughout the entire chain execution process.
• D. Only after the output has been generated.

正解：B

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 # 41
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

• A. Step-Back Prompting
• B. Chain-of-Thought
• C. In-Context Learning
• D. Least-to-Most Prompting

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting explicitly instructs an LLM to provide intermediate reasoning steps, enhancing complex task performance-Option B is correct. Option A (Step-Back) reframes problems, not emits steps. Option C (Least-to-Most) breaks tasks into subtasks, not necessarily showing reasoning. Option D (In-Context Learning) uses examples, not reasoning steps. CoT improves transparency and accuracy.
OCI 2025 Generative AI documentation likely covers CoT under advanced prompting techniques.

質問 # 42
Why is normalization of vectors important before indexing in a hybrid search system?

• A. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
• B. It converts all sparse vectors to dense vectors.
• C. It significantly reduces the size of the database.
• D. It ensures that all vectors represent keywords only.

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.

質問 # 43
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

• A. It updates all the weights of the model uniformly.
• B. It selectively updates only a fraction of weights to reduce the number of parameters.
• C. It increases the training time as compared to Vanilla fine-tuning.
• D. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning (a Parameter-Efficient Fine-Tuning method) updates a small subset of the model's weights, reducing computational cost and mitigating overfitting compared to Vanilla fine-tuning, which updates all weights. This makes Option C correct. Option A describes Vanilla fine-tuning, not T-Few. Option B is incomplete, as it omits the overfitting benefit. Option D is false, as T-Few typically reduces training time due to fewer updates. T-Few balances efficiency and performance.
OCI 2025 Generative AI documentation likely describes T-Few under fine-tuningoptions.

質問 # 44
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. Based on the number of images and videos contained in the documents
• C. By the complexity of language used in the documents
• D. According to the length of the documents

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
In basic keyword-based search, documents are evaluated by matching user-provided keywords, with relevance often determined by their presence and frequency (e.g., term frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is unrelated to simple keyword search. Option B (multimedia) isn't considered in text-based keyword methods. Option D (length) may influence scoring indirectly but isn't the primary metric. Keyword search prioritizes exact matches.
OCI 2025 Generative AI documentation likely contrasts keyword search with semantic search under retrieval methods.

質問 # 45
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

• A. To store text in an external database without using it for generation
• B. To generate text using extra information obtained from an external data source
• C. To generate text based only on the model's internal knowledge without external data
• D. To retrieve text from an external source and present it without any modifications

正解：B

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 # 46
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

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

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.

質問 # 47
In the simplified workflow for managing and querying vector data, what is the role of indexing?

• A. To categorize vectors based on their originating data type (text, images, audio)
• B. To compress vector data for minimized storage usage
• C. To map vectors to a data structure for faster searching, enabling efficient retrieval
• D. To convert vectors into a non-indexed format for easier retrieval

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation=
Indexing in vector databases maps high-dimensional vectors to a data structure (e.g., HNSW,Annoy) to enable fast, efficient similarity searches, critical for real-time retrieval in LLMs. This makes Option B correct. Option A is backwards-indexing organizes, not de-indexes. Option C (compression) is a side benefit, not the primary role. Option D (categorization) isn't indexing's purpose-it's about search efficiency. Indexing powers scalable vector queries.
OCI 2025 Generative AI documentation likely explains indexing under vector database operations.

質問 # 48
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

• A. Evaluates the performance metrics of the custom models
• B. Serves as a designated point for user requests and model responses
• C. Updates the weights of the base model during the fine-tuning process
• D. Hosts the training data for fine-tuning custom models

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation=
A "model endpoint" in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model-Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.
OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.

質問 # 49
Which is NOT a built-in memory type in LangChain?

• A. ConversationImageMemory
• B. ConversationSummaryMemory
• C. ConversationTokenBufferMemory
• D. ConversationBufferMemory

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)-Options B, C, and D are valid. ConversationImageMemory (A) isn't a standard type-image handling typically requires custom or multimodal extensions, not a built-in memory class-making A correct as NOT included.
OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.

質問 # 50
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 shared across user sessions.
• C. A given StreamlitChatMessageHistory will NOT be persisted.
• D. StreamlitChatMessageHistory can be used in any type of LLM application.

正解：D

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 # 51
What is LangChain?

• A. A JavaScript library for natural language processing
• B. A Ruby library for text generation
• C. A Python library for building applications with Large Language Models
• D. A Java library for text summarization

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation=
LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone-it's broader in scope. It's widely used for LLM-powered apps.
OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.

質問 # 52
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

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

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation=
The "stop sequence" parameter defines a string (e.g., "." or "\n") that, when generated, halts text generation, allowing control over output length or structure-Option A is correct. Option B (penalty) describes frequency/presence penalties. Option C (max tokens) is a separate parameter. Option D (randomness) relates to temperature. Stop sequences ensure precise termination.
OCI 2025 Generative AI documentation likely details stop sequences under generation parameters.

質問 # 53
What does accuracy measure in the context of fine-tuning results for a generative model?

• A. The proportion of incorrect predictions made by the model during an evaluation
• B. How many predictions the model made correctly out of all the predictions in an evaluation
• C. The number of predictions a model makes, regardless of whether they are correct or incorrect
• D. The depth of the neural network layers used in the model

正解：B

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 # 54
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

• A. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back
• B. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."
• C. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.

質問 # 55
......

100%無料1Z0-1127-25日常練習試験には90問があります：https://www.jpntest.com/shiken/1Z0-1127-25-mondaishu

1Z0-1127-25試験資料Oracle学習ガイド：https://drive.google.com/open?id=1CK7-wy_oR3TylYPy4bhk4swRzjZVHHGp