高效的Oracle 1Z0-184-25證照考試是行業領先材料＆最佳的1Z0-184-25：Oracle AI Vector Search Professional

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Oracle 1Z0-184-25 考試大綱：

主題	簡介
主題 1	Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.
主題 2	Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.
主題 3	Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.

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最新的 Oracle Database 23ai 1Z0-184-25 免費考試真題 (Q54-Q59):

問題 #54
When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?

A. Rely on SQL*Loader's automatic normalization of vector data
B. Enclose vector components in curly braces ({})
C. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient
D. Use sparse format for vector data

答案：B

解題說明：
SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type's expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn't supported for VECTOR columns, which require dense arrays. SQLLoader doesn't normalize vectors automatically (D); formatting must be explicit. Oracle's documentation specifies curly braces for CSV-loaded vectors.

問題 #55
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

A. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
B. DBMS_VECTOR.TEXT_TO_PLAIN
C. DBMS_VECTOR.CONVERT_TO_TEXT
D. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

答案：A

解題說明：
In Oracle Database 23ai, DBMS_VECTOR_CHAIN.UTL_TO_TEXT is the PL/SQL function that converts documents in formats like PDF, DOC, JSON, XML, or HTML into plain text, a key step in preparing data for vectorization in RAG workflows. DBMS_VECTOR.TEXT_TO_PLAIN (A) is not a valid function. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS (C) splits text into smaller segments, not converts documents. DBMS_VECTOR.CONVERT_TO_TEXT (D) does not exist in the documented packages. UTL_TO_TEXT is part of the DBMS_VECTOR_CHAIN package, designed for vector processing pipelines, and is explicitly noted for document conversion in Oracle's documentation.

問題 #56
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
B. It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
C. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers
D. It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights

答案：A

解題說明：
RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts. This (A) improves context-awareness and precision, leveraging enterprise-specific data without retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training specialized LLMs (C) is not RAG's purpose; it uses existing models. Real-time streaming (D) is possible but not the primary benefit, as RAG focuses on stored data retrieval. Oracle's RAG documentation emphasizes private data integration for better LLM outputs.

問題 #57
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

A. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
B. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
C. By requiring users to manually enter their OCI API keys each time they execute a natural language query
D. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials

答案：D

解題說明：
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.

問題 #58
Why would you choose to NOT define a specific size for the VECTOR column during development?

A. Different external embedding models produce vectors with varying dimensions and data types
B. It limits the length of text that can be vectorized
C. It impacts the accuracy of similarity searches
D. It restricts the database to a single embedding model

答案：A

解題說明：
In Oracle Database 23ai, a VECTOR column can be defined with a specific size (e.g., VECTOR(512, FLOAT32)) or left unspecified (e.g., VECTOR). Not defining a size (D) provides flexibility during development because different embedding models (e.g., BERT, SentenceTransformer) generate vectors with varying dimensions (e.g., 768, 384) and data types (e.g., FLOAT32, INT8). This avoids locking the schema into one model, allowing experimentation. Accuracy (A) isn't directly impacted by size definition; it depends on the model and metric. A fixed size doesn't restrict the database to one model (B) but requires matching dimensions. Text length (C) affects tokenization, not vector dimensions. Oracle's documentation supports undefined VECTOR columns for flexibility in AI workflows.

問題 #59
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高效的Oracle 1Z0-184-25證照考試是行業領先材料＆最佳的1Z0-184-25：Oracle AI Vector Search Professional

Oracle 1Z0-184-25 考試大綱：

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最新的 Oracle Database 23ai 1Z0-184-25 免費考試真題 (Q54-Q59):

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