Prueba querys

What happens when querying with an IVF index if you increase the value of the NEIGHBOR PARTITION PROBES parameter?. More partitions are probed, improving accuracy, but also increasing query latency. The number of centroids decreases. Index creation time is reduced. Accuracy decreases.

What is the purpose of the Vector Pool in Oracle Database 23c AI?. To cache vector data for faster query processing. To store HNSW vector indexes and IVF index metadata. To manage vector operations and computations. To store vector embeddings for similarity search.

Which SQL operation is NOT supported when working with VECTOR columns in Oracle Database 23c?. Loading data with SQL*Loader. Distance computations using custom distance functions. INSERT INTO statements. JOIN conditions.

A retail company uses an Oracle Database 23c HNSW vector index to recommend products to customers based on their browsing history. The database administrator notices that after restarting the database, product recommendations are slower. What steps should the administrator take to resolve the issue?. Modify the distance metric to DOT. Rebuild the HNSW index or enable automatic reload. Adjust the NEIGHBOR_PARTITION_PROBES parameter for improved accuracy. Decrease the VECTOR_MEMORY_SIZE parameter.

What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23c?. HNSW uses a flat structure for indexing vectors, whereas IVF uses a hierarchical structure. IVF is suitable for high-dimensional vectors, whereas HNSW is better for low-dimensional vectors. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions. IVF supports exact similarity searches, whereas HNSW only supports approximate searches.

Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?. NEIGHBORS. EFCONSTRUCTION. VECTOR_MEMORY_SIZE. TARGET_ACCURACY.

What is a key advantage of using GoldenGate 23c AI for managing and distributing vector data for AI applications?. Built-in version control for vector data. Automatic translation of vector embeddings between formats. Specialized vector embedding compression. Real-time vector data updates across locations.

You need to generate a vector from the string ‘[1.2, 3.4]’ in FLOAT32 format with 2 dimensions. Which function will you use?. TO_VECTOR. VECTOR_SERIALIZE. VECTOR_DISTANCE. FROM_VECTOR.

What is a key advantage of generating vector embeddings outside the database?. Flexibility in choosing specialized embedding models. Reduced storage requirements. Improved data security. Simplified data management.

In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?. It improves the accuracy of the LLM by providing additional training data. It speeds up the vectorization process by providing a unique identifier for each chunk. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file. It helps differentiate between chunks from different files but has no impact on vectorization.

If a query vector uses a different distance metric than the one used to create the index, what happens?. The query uses a default distance metric. The query fails. The query is automatically converted to use the correct distance metric. The query returns inaccurate results.

What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?. It specifies the type of vector encoding used in the database. It converts the vectors to a format compatible with the SQL database. It indicates that the cosine distance metric should be used to measure similarity between vectors. It filters out vectors with a cosine similarity below a certain threshold.

You are working with vector search in Oracle Database 23c AI and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?. The physical storage location of the vector data. Regularly updating vector embeddings to reflect changes in the source data. Using the same embedding model for both vector creation and similarity search. The specific distance algorithm employed for vector comparisons.

Which function should you use to determine the storage format of a vector?. VECTOR_DIMENSION_FORMAT. VECTOR_LNORM. VECTOR_EMBEDDING. VECTOR_CHUNKS.

Which is a type of Neighbor Partition Vector Index supported in Oracle Database 23c?. Hierarchical Navigable Small World (HNSW). Inverted File Flat (IVF). Binary Tree Index. Neighbor Hash Table (NHT).

Which vector index available in Oracle Database 23c is known for its speed and accuracy, making it a preferred choice for vector search?. Binary Tree (BT) index. Hierarchical Navigable Small World (HNSW) index. Inverted File Flat (IVF) index. Inverted File System (IFS) index.

In Oracle Database 23c AI, which SQL function is used to split text into words, sentences, or paragraphs for vector embedding preparation?. VECTOR_NORM. VECTOR_DISTANCE. VECTOR_CHUNKS. VECTOR_EMBEDDING.

Which Python library is used to vectorize text chunks and the user's question in the following example?. oracle-database. numpy. sentence-transformers. pandas.

You are tasked with creating a table to store vector embeddings with the following characteristics. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8));. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512));. Each vector must have exactly 512 dimensions. The dimensions should be stored as 32-bit floating-point numbers. CREATE TABLE vectors (id NUMBER, embedding VECTOR);. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32));.

When generating vector embeddings for a new dataset outside of Oracle Database 23c, which factor is crucial to ensure meaningful similarity search results?. The same vector embedding model must be used for vectorizing the data and creating a query vector. The choice of programming language used to process the dataset (for example, Python, Java). The physical location where the vector embeddings are stored. The storage format of the new dataset (for example, CSV, JSON).

Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23c AI?. Modifying the data type of an existing VECTOR column to a non-VECTOR type. Creating a new table using CTAS CREATE TABLE AS SELECT that includes the VECTOR column from the original table. Adding a new VECTOR column to the table. Dropping an existing VECTOR column from the table.

What is the primary function of an embedding model in the context of vector search?. To define the schema for a vector database. To store vectors in a structured format for efficient retrieval. To execute similarity search operations within a database. To transform text or data into numerical vector representations.

What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23c AI similarity search?. To group vectors by their exact scores. To fetch rows that match exact vector embeddings. To create vector indexes for efficient searches. To calculate the distance between vectors using a specified metric.

In Oracle Database 23c AI, which SQL function calculates the distance between two vectors using the Euclidean metric?. HAMMING_DISTANCE. L2_DISTANCE. L1_DISTANCE. COSINE_DISTANCE.

Which statement best describes the capability of Oracle Data Pump for handling vector data in the context of vector search applications?. Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search. Data Pump can only export and import vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database. Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications.

Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?. OCI Data Science. OCI Generative AI. OCI Vision. OCI Language.

Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23c AI?. EMBED_TEXT. CREATE_VECTOR_EMBEDDING. VECTOR_EMBEDDING. GENERATE_EMBEDDING.

You need to prioritize accuracy over speed in a similarity search for a dataset of s. Which approach should you use?. Approximate similarity search with HNSW indexing and target accuracy of 70%. Exact similarity search using a full table scan. Multivector similarity search with partitioning. Approximate similarity search with IVF indexing and target accuracy of 70%.

Why would you choose to NOT define a specific size for the VECTOR column during development?. Different external embedding models produce vectors with varying dimensions and data types. It restricts the database to a single embedding model. It limits the length of text that can be vectorized. It impacts the accuracy of similarity searches.

An application needs to fetch the top-3 matching sentences from a dataset of books while ensuring a balance between speed and accuracy. Which query structure should you use?. Multivector similarity search with approximate fetching and target accuracy. Exact similarity search with Euclidean distance. Approximate similarity search with the VECTOR_DISTANCE function. A combination of relational filters and similarity search.

You are tasked with finding the closest matching sentences across books, where each book has multiple paragraphs and sentences. Which SQL structure should you use?. A nested query with ORDER BY. Exact similarity search with a single query vector. FETCH PARTITIONS BY clause. GROUP BY with vector operations.

What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle Database 23c AI?. It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information. It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially limiting the breadth of applications. It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM. It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times and lower storage requirements.

How does Select AI RAG leverage vector stores to enhance the capabilities of Large Language Models (LLMs)?. Select AI RAG uses vector stores to reduce the size of LLM prompts by retrieving only the most relevant information, minimizing computational overhead. Select AI RAG employs vector stores to isolate sensitive data, ensuring LLMs do not have direct access to private information. Select AI RAG leverages vector stores to perform semantic similarity searches, finding and adding pertinent information from private data to improve prompts for LLMs. Select AI RAG utilizes vector stores for the efficient management and retrieval of large volumes of textual data, leading to faster response times from LLMs.

Which SQL statement correctly adds a VECTOR column named v with 4 dimensions and FLOAT32 format to an existing table named my_table?. ALTER TABLE my_table MODIFY v VECTOR(4, FLOAT32);. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32));. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32);. UPDATE my_table SET v = VECTOR(4, FLOAT32);.

You are tasked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?. Use VECTOR_INDEX_HINT and NO_WHERE clause. Perform the similarity search without a WHERE clause. Use UNION ALL with vector operations. Apply relational filters and a similarity search in the query.

What does a target accuracy of 80% in an approximate similarity search imply?. The search will process 80% of the dataset. 80% of the query results will match the exact search results. Only 80% of the indexed vectors are used. 80% accuracy seen in the index calculated distances.

What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?. It supports hierarchical partitioning of vectors. It guarantees higher accuracy than Euclidean Distance. It is simpler and faster because it avoids square-root calculations. It is the default distance metric for Oracle AI Vector Search.

In Oracle Database 23c AI, which data type is used to store vector embeddings for similarity search?. VECTOR2. VECTOR. BLOB. VARCHAR2.

What is the significance of using local ONNX models for embedding within the database?. Support for legacy SQL*Plus clients. Improved accuracy compared to external models. Reduced embedding dimensions for faster processing. Enhanced security because data remains within the database.

How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt. Clusters similar text chunks and randomly selects one from the most relevant cluster. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM. Converts the question to keywords, searches for matches, and inserts the text into the response.

Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?. ODMS_VECTOR_CHAIN.UTIL_TO_TEXT. ODMS_VECTOR_CHAIN.UTIL_TO_TEXT. ODMS_VECTOR.TEXT_TO_PLAIN. ODMS_VECTOR.CONVERT_TO_TEXT.

Which SQL query would retrieve the top 10 vectors based on Euclidean distance using exact similarity search?. SELECT docID FROM vector_tab ORDER BY VECTOR_DISTANCE(embedding, query_vector, EUCLIDEAN). SELECT docID FROM vector_tab GROUP BY VECTOR_DISTANCE(embedding, query_vector, EUCLIDEAN). SELECT docID FROM vector_tab ORDER BY VECTOR_DISTANCE(embedding, query_vector, COSINE). SELECT docID FROM vector_tab WHERE VECTOR_DISTANCE(embedding, query_vector, EUCLIDEAN) < 10;.

Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23c AI?. ODMS_GENAI. ODMS_ML. ODMS_AI. ODMS_VECTOR_CHAIN.

What is the primary purpose of the DBMS_VECTOR_CHAIN.UTIL_TO_CHUNKS package in a RAG application?. To generate vector embeddings from a text document. To load a document into the database. To split a large document into smaller chunks to improve vector quality by minimizing token truncation. To convert a document into a single, large text string.

When using SQLLoader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input file?. 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. Rely on SQLLoader's automatic normalization of vector data. Enclose vector components in curly braces ⓐ. Use sparse format for vector data.

Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23c AI?. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses. 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. 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. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing the reliance on external AI providers.

What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?. Cosine. Hamming. Manhattan. Euclidean.

Which is NOT a feature or capability related to AI and Vector Search in Exadata?. AI Smart Scan. Vector Replication with GoldenGate. Loading Vector Data using SQL*Loader. Native Support for Vector Search Only within the Database Server.