Oracle 1Z0-1127-25 問題集

質問 1：
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. Temporal relationships; necessary for predicting future linguistic trends
B. Semantic relationships; crucial for understanding context and generating precise language
C. Hierarchical relationships; important for structuring database queries
D. Linear relationships; they simplify the modeling process
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 2：
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?
A. Underfitting
B. Overfitting
C. Data Leakage
D. Model Drift
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?
A. Least-to-Most Prompting
B. In-context Learning
C. Step-Back Prompting
D. Chain-of-Thought
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
Which is NOT a typical use case for LangSmith Evaluators?
A. Aligning code readability
B. Detecting bias or toxicity
C. Evaluating factual accuracy of outputs
D. Measuring coherence of generated text
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 5：
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?
A. GPUs are shared with other customers to maximize resource utilization.
B. GPUs are used exclusively for storing large datasets, not for computation.
C. Each customer's GPUs are connected via a public Internet network for ease of access.
D. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Why is it challenging to apply diffusion models to text generation?
A. Because text representation is categorical unlike images
B. Because text generation does not require complex models
C. Because text is not categorical
D. Because diffusion models can only produce images
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 7：
What is LangChain?
A. A Python library for building applications with Large Language Models
B. A Ruby library for text generation
C. A JavaScript library for natural language processing
D. A Java library for text summarization
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 8：
What is the primary purpose of LangSmith Tracing?
A. To analyze the reasoning process of language models
B. To debug issues in language model outputs
C. To monitor the performance of language models
D. To generate test cases for language models
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 9：
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
A. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
B. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
C. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
D. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
正解：B
解説: (Topexam メンバーにのみ表示されます)