Explanation: 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. How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
Explanation: Comprehensive and Detailed In -Depth Explanation= A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite — penalizing unused tokens isn’t the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety. : OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters. What does "k -shot prompting" refer to when using Large Language Models for task -specific applications?
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn’t a primary focus —embedding quality is. Option C (syntactic clustering) is too narrow —semantics drives improvement. Option D (translation) isn’t an embedding model’s role. v3 boosts RAG effectiveness. : OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements. Which statement best describes the role of encoder and decoder models in natural language processing?
LangSmith Tracing is a tool for debugging and understanding LLM applications by tracking inputs, outputs, and intermediate steps, helping identify issues in complex chains. This makes Option C correct. Option A (test cases) is a secondary use, not primary. Option B (reasoning) overlaps but isn’t the core focus —debugging is. Option D (performance) is broader —tracing targets specific issues. It’s essential for development transparency. : OCI 2025 Generative AI documentation likely covers LangSmith under debugging or monitoring tools. Why is normalization of vectors important before indexing in a hybrid search system?
images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image -to-text with appropriate extensions, making Option A correct. Option B (LLM) is text -only. Option C (token -based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs. : OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications. Which is a cost -related benefit of using vector databases with Large Language Models (LLMs)?
➢ TOTAL QUESTIONS: 168 What is the role of temperature in the decoding process of a Large Language Model (LLM)?
