B. Increasing the vector dimensionality to improve overall retrieval accuracy C. Reducing the number of nearest neighbors retrieved per search execution D. Applying multiple indexing structures to enhance memory optimization Explanation: Reducing the number of nearest neighbors improves search speed by limiting candidate comparisons. Increasing vector dimensionality, applying multiple indexes, or prioritizing deterministic algorithms do not optimize speed effectively. Which factor significantly impacts the trade -off between retrieval speed and accuracy in IVF search?
for NoSQL applications, and Cloud Object Storage is primarily used for unstructured file storage rather than vector search. Which optimization strategy can improve the performance of exact similarity search without reducing accuracy?
connection = oracledb.connect(user=un, password=pw, dsn=ds) table_name = "Page" with connection.cursor() as cursor: create_table_sql = f""" CREATE TABLE IF NOT EXISTS {table_name} ( id NUMBER PRIMARY KEY, payload CLOB CHECK (payload IS JSON), vector VECTOR )""" try: cursor.execute(create_table_sql) except oracledb.DatabaseError as e: raise connection.autocommit = True from sentence_transformers import SentenceTransformer encoder = SentenceTransformer('all -MiniLM -L12-v2')
Explanation: Stored functions can filter documents based on query relevance, ensuring accurate and efficient retrieval. PL/SQL collections are not optimized for vector search, NoSQL databases do not fully integrate with PL/SQL, and triggers add unnecessary complexity. Which indexing method is commonly used for approximate similarity search in high-dimensional vector spaces?
Which Oracle feature enhances vector embedding storage without affecting retrieval speed?
What is the primary limitation of using Euclidean distance for similarity search?
