[2025年09月] 最新のOracle 1Z0-1127-25認定練習テスト問題
確認済み1Z0-1127-25問題集と解答で一年間無料最速更新
質問 # 25
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
- A. Temperature has no effect on the probability distribution; it only changes the speed of decoding.
- B. Increasing temperature flattens the distribution, allowing for more varied word choices.
- C. Decreasing temperature broadens the distribution, making less likely words more probable.
- D. Increasing temperature removes the impact of the most likely word.
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
Temperature controls the randomness of an LLM's output by adjusting the softmax probability distribution over the vocabulary. Increasing temperature (e.g., to 1.5) flattens the distribution, reducing the dominance of high-probability words and allowing more diverse, less predictable choices, making Option C correct. Option A is misleading-higher temperature doesn't remove the top word's impact entirely but reduces its relative likelihood. Option B is incorrect, as decreasing temperature sharpens the distribution, favoring likely words, not broadening it. Option D is false, as temperature directly affects the distribution, not just decoding speed. This mechanism is key for balancing creativity and coherence.
OCI 2025 Generative AI documentation likely explains temperature under decoding or output control parameters.
質問 # 26
What is LangChain?
- A. A Ruby library for text generation
- B. A Python library for building applications with Large Language Models
- C. A Java library for text summarization
- D. A JavaScript library for natural language processing
正解：B
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 # 27
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
- A. Ranker
- B. Generator
- C. Encoder-Decoder
- D. Retriever
正解：A
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 # 28
What does the RAG Sequence model do in the context of generating a response?
- A. It retrieves relevant documents only for the initial part of the query and ignores the rest.
- B. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
- C. It modifies the input query before retrieving relevant documents to ensure a diverse response.
- D. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
The RAG (Retrieval-Augmented Generation) Sequence model retrieves a set of relevant documents for a query from an external knowledge base (e.g., via a vector database) and uses them collectively with the LLM to generate a cohesive, informed response. This leverages multiple sources for better context, making Option B correct. Option A describes a simpler approach (e.g., RAG Token), not Sequence. Option C is incorrect-RAG considers the full query. Option D is false-query modification isn't standard in RAG Sequence. This method enhances response quality with diverse inputs.
OCI 2025 Generative AI documentation likely details RAG Sequence under retrieval-augmented techniques.
質問 # 29
How does the structure of vector databases differ from traditional relational databases?
- A. A vector database stores data in a linear or tabular format.
- B. It is not optimized for high-dimensional spaces.
- C. It is based on distances and similarities in a vector space.
- D. It uses simple row-based data storage.
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
Vector databases store data as high-dimensional vectors, optimized for similarity searches (e.g., cosine distance), unlike relational databases' tabular, row-column structure. This makes Option C correct. Option A and D describe relational databases. Option B is false-vector databases excel in high-dimensional spaces. Vector databases support semantic queries critical for LLMs.
OCI 2025 Generative AI documentation likely contrasts these under data storage options.
質問 # 30
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?
- A. They always use an external database for generating responses.
- B. They use vector databases exclusively to produce answers.
- C. They rely on internal knowledge learned during pretraining on a large text corpus.
- D. They cannot generate responses without fine-tuning.
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.
質問 # 31
In which scenario is soft prompting especially appropriate compared to other training styles?
- A. When there is a significant amount of labeled, task-specific data available.
- B. When the model needs to be adapted to perform well in a different domain it was not originally trained on.
- C. When the model requires continued pre-training on unlabeled data.
- D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.
OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.
質問 # 32
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?
- A. Stored in Key Management service
- B. Stored in an unencrypted form in Object Storage
- C. Shared among multiple customers for efficiency
- D. Stored in Object Storage encrypted by default
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices-Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.
OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.
質問 # 33
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 # 34
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
- A. The process of training the model on k different tasks simultaneously to improve its versatility
- B. Limiting the model to only k possible outcomes or answers for a given task
- C. Providing the exact k words in the prompt to guide the model's response
- D. Explicitly providing k examples of the intended task in the prompt to guide the model's output
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.
質問 # 35
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?
- A. The token will be the only one considered in the next generation step.
- B. The token is less likely to follow the current token.
- C. The token is more likely to follow the current token.
- D. The token is unrelated to the current token and will not be used.
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
In "Show Likelihoods," a higher number (probability score) indicates a token's greater likelihood of following the current token, reflecting the model's prediction confidence-Option B is correct. Option A (less likely) is the opposite. Option C (unrelated) misinterprets-likelihood ties tokens contextually. Option D (only one) assumes greedy decoding, not the feature's purpose. This helps users understand model preferences.
OCI 2025 Generative AI documentation likely explains "Show Likelihoods" under token generation insights.
質問 # 36
Why is it challenging to apply diffusion models to text generation?
- A. Because text is not categorical
- B. Because diffusion models can only produce images
- C. Because text generation does not require complex models
- D. Because text representation is categorical unlike images
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false-text generation can benefit from complex models. Option B is incorrect-text is categorical. Option D is wrong, as diffusion models aren't inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it's less straightforward.
OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.
質問 # 37
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?
- A. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
- B. 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.
- C. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
- D. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
正解：B
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 # 38
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?
- 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 encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn't focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.
OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.
質問 # 39
Which statement best describes the role of encoder and decoder models in natural language processing?
- A. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.
- B. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
- C. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
- D. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation=
In NLP (e.g., transformers), encoders convert input text into a vector representation (encoding meaning), while decoders generate text from such vectors (e.g., in translation or generation). This makes Option C correct. Option A is false-decoders generate text. Option B reverses roles-encoders don't predict next words, and decoders don't encode. Option D oversimplifies-encoders handle text, not just numbers. This is the foundation of seq2seq models.
OCI 2025 Generative AI documentation likely explains encoder-decoder roles under model architecture.
質問 # 40
What is the function of "Prompts" in the chatbot system?
- A. They are used to initiate and guide the chatbot's responses.
- B. They are responsible for the underlying mechanics of the chatbot.
- C. They store the chatbot's linguistic knowledge.
- D. They handle the chatbot's memory and recall abilities.
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation=
Prompts in a chatbot system are inputs provided to the LLM to initiate and steer its responses, often including instructions, context, or examples. They shape the chatbot's behavior without altering its core mechanics, making Option B correct. Option A is false, as knowledge is stored in the model's parameters. Option C relates to the model's architecture, not prompts. Option D pertains to memory systems, not prompts directly. Prompts are key for effective interaction.
OCI 2025 Generative AI documentation likely covers prompts under chatbot design or inference sections.
質問 # 41
How are prompt templates typically designed for language models?
- A. As predefined recipes that guide the generation of language model prompts
- B. To be used without any modification or customization
- C. To work only with numerical data instead of textual content
- D. As complex algorithms that require manual compilation
正解：A
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 # 42
Which is NOT a typical use case for LangSmith Evaluators?
- A. Measuring coherence of generated text
- B. Aligning code readability
- C. Evaluating factual accuracy of outputs
- D. Detecting bias or toxicity
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.
質問 # 43
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
- A. "Top p" selects tokens from the "Top k" tokens sorted by probability.
- B. "Top p" determines the maximum number of tokens per response.
- C. "Top p" limits token selection based on the sum of their probabilities.
- D. "Top p" assigns penalties to frequently occurring tokens.
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) 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 # 44
Which statement is true about string prompt templates and their capability regarding variables?
- A. They require a minimum of two variables to function properly.
- B. They can only support a single variable at a time.
- C. They are unable to use any variables.
- D. They support any number of variables, including the possibility of having none.
正解：D
解説：
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.
質問 # 45
Which is the main characteristic of greedy decoding in the context of language model word prediction?
- A. It selects words based on a flattened distribution over the vocabulary.
- B. It picks the most likely word at each step of decoding.
- C. It chooses words randomly from the set of less probable candidates.
- D. It requires a large temperature setting to ensure diverse word selection.
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, optimizing locally without lookahead, making Option D correct. Option A (random low-probability) contradicts greedy's deterministic nature. Option B (high temperature) flattens distributions for diversity, not greediness. Option C (flattened distribution) aligns with sampling, not greedy decoding. Greedy is simple but can lack global coherence.
OCI 2025 Generative AI documentation likely describes greedy decoding under decoding strategies.
質問 # 46
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?
- A. Hierarchical relationships; important for structuring database queries
- B. Semantic relationships; crucial for understanding context and generating precise language
- C. Linear relationships; they simplify the modeling process
- D. Temporal relationships; necessary for predicting future linguistic trends
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
Vector databases store embeddings that preserve semantic relationships (e.g., similarity between "dog" and "puppy") via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn't the focus-semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.
OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.
質問 # 47
What is prompt engineering in the context of Large Language Models (LLMs)?
- A. Training the model on a large dataset
- B. Iteratively refining the ask to elicit a desired response
- C. Adding more layers to the neural network
- D. Adjusting the hyperparameters of the model
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
Prompt engineering involves crafting and refining input prompts to guide an LLM to produce desired outputs without altering its internal structure or parameters. It's an iterative process that leverages the model's pre-trained knowledge, making Option A correct. Option B is unrelated, as adding layers pertains to model architecture design, not prompting. Option C refers to hyperparameter tuning (e.g., temperature), not prompt engineering. Option D describes pretraining or fine-tuning, not prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt engineering in sections on model interaction or inference.
質問 # 48
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
- A. It selectively updates only a fraction of the model's weights.
- B. It updates all the weights of the model uniformly.
- C. It does not update any weights but restructures the model architecture.
- D. It increases the training time as compared to Vanilla fine-tuning.
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM's weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false-T-Few updates weights, not architecture. Option D is incorrect-T-Few typically reduces training time. T-Few optimizes efficiency.
OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.
質問 # 49
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