Which method improves performance when training embedding models inside Oracle’s database?
Proper field delimiters and consistent formatting are crucial for accurate parsing and successful vector ingestion with SQL Loader. Query -driven pre-processing, JSON storage, and in-memory indexing help in other areas but do not ensure accurate vector data import. Which best describes the role of partition centroids in an IVF index during search?
Explanation: The trade -off between accuracy and speed depends on the number of neighbors retrieved. More neighbors improve recall but increase computation. Query expansion, vector transformation, and deterministic traversal do not directly control this balance. Which method enhances Oracle Data Pump’s efficiency for unloading vector datasets?
C. Jaccard similarity, which evaluates overlap in categorical attributes D. Euclidean distance, which measures direct vector differences in space Explanation: Euclidean distance is widely used in exact similarity search because it provides precise measurements of vector differences in continuous space. Manhattan distance is less common for high -dimensional data, Jaccard similarity is for categorical attributes, and cosine distance is often used in approximate indexing. Which challenge arises when updating indexed vector columns?
Explanation: In Oracle Database 23ai, the VECTOR_DISTANCE function calculates the distance between two vectors using a specified metric. The COSINE parameter in the query (vector_distance(vector, :vector, COSINE)) instructs the database to use the cosine distance metric (C) to measure similarity. Cosine distance, defined as 1 - cosine similarity, is ideal for high -dimensional vectors (e.g., text embeddings) as it focuses on angular separation rather than magnitude. It doesn’t filter vectors (A); filtering requires additional conditions (e.g., WHERE clause). It doesn’t convert vector formats (B); vectors are already in the VECTOR type. It also doesn’t specify encoding (D), which is defined during vector creation (e.g., FLOAT32). Oracle’s documentation confirms COSINE as one of the supported metrics for similarity search. Reference: Oracle Database 23ai SQL Language Reference, Section on VECTOR_DISTANCE. 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? bash CollapseWrapCopy docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections] # Sample the resulting data docs[:2]
Which method should be used to ensure efficient computation of embeddings inside Oracle?
