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所在平台: Udemy |
课程主页: https://www.udemy.com/course/1z0-184-25-oracle-ai-vector-search-professional-practiceexam/
课程评论：没有评论
以下是对Coursera课程“1Z0-184-25 Oracle AI Vector Search Professional PracticeExam”的内容总结： 本课程是一套实践考试，旨在帮助学员掌握利用Oracle的AI向量搜索能力所必需的关键技能。其内容覆盖了向量搜索的核心概念和技术，包括： * **向量基础知识 (20%)**：理解和运用向量数据类型进行语义查询，熟悉各种向量距离函数（如余弦相似度、欧氏距离），并进行向量数据的DDL和DML操作，为AI驱动的分析奠定基础。 * **向量索引 (15%)**：学习构建Hierarchical Navigable Small World (HNSW) 和 Inverted File (IVF) 等向量索引，以优化大规模向量数据集的搜索性能。 * **相似性搜索 (15%)**：掌握精确搜索、近似搜索和多向量相似性搜索，并通过向量索引实现高效的数据检索。 * **向量嵌入 (15%)**：学习在Oracle数据库内或外部创建和管理向量嵌入，并将其与机器学习模型集成，以增强数据表示能力。 * **构建RAG应用 (25%)**：深入了解检索增强生成（RAG）的概念，并使用PL/SQL和Python开发相关应用，将向量搜索与生成式AI相结合，交付上下文感知型解决方案。 * **相关AI能力 (10%)**：学习使用Exadata AI Storage、Select AI进行自然语言查询、SQL Loader和Oracle Data Pump等工具，以简化向量数据管理并增强AI集成。 **课程目标**： 本实践考试课程将为学员提供运用Oracle AI驱动的向量搜索能力的先进技能，赋能他们在Oracle数据库中进行高级数据处理。 **先决条件**： * 对Oracle Database概念和SQL有基本了解。 * 熟悉Python或PL/SQL编程。 * 具备AI和机器学习基础知识会更有帮助，但非必需。 **适用人群**： 希望在Oracle AI向量搜索技术领域提升专业知识的专业人士。 **免责声明**： Oracle和Java是Oracle及其关联公司的注册商标。本课程未得到Oracle公司认可或赞助。
Practice Exam covers essential skills for leveraging Oracle's AI vector search capabilities, focusing on vector fundamentals, indexing, similarity search, embeddings, Retrieval-Augmented Generation (RAG) applications, and related AI functionalities. Designed to align with exam objectives, the course ensures a comprehensive understanding of AI-driven data processing within Oracle databases'.Vector Fundamentals (20%): Master vector data types to execute semantic queries, apply vector distance functions (e.g., cosine, Euclidean), and perform Data Manipulation Language (DML) and Data Definition Language (DDL) operations on vector data for robust AI-driven analytics.Vector Indexes (15%): Build Hierarchical Navigable Small World (HNSW) and Inverted File (IVF) vector indexes to optimize search performance, ensuring efficient handling of large-scale vector datasets.Similarity Search (15%): Conduct exact, approximate, and multi-vector similarity searches leveraging vector indexes, enabling precise and scalable data retrieval.Vector Embeddings (15%): Create and manage vector embeddings inside Oracle databases or externally, integrating with machine learning models for enhanced data representation.Building a RAG Application (25%): Explore RAG concepts and develop applications using PL/SQL and Python, combining vector search with generative AI to deliver context-aware solutions.Related AI Capabilities (10%): Utilize Exadata AI Storage, Select AI for natural language querying, SQL Loader, and Oracle Data Pump to streamline vector data management and enhance AI integration.Practice Exam Course equips students with cutting-edge skills to harness Oracle's AI-driven vector search capabilities, enabling advanced data processing within Oracle databases. covering critical areas such as vector fundamentals, indexing, similarity search, embeddings, Retrieval-Augmented Generation (RAG) applications, and related AI functionalities.PrerequisitesBasic understanding of Oracle Database concepts and SQL.Familiarity with Python or PL/SQL programming.Knowledge of AI and machine learning basics is helpful but not required.This course is ideal for professionals seeking to advance their expertise in Oracle's AI vector search technologies, offering hands-on practice and exam-focused insights.Disclaimer: Oracle and Java are registered trademarks of Oracle and/or its affiliates. This course is not endorsed or sponsored by Oracle Corporation.
