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Oracle AI Vector Search Professional Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

A. 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
B. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
C. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database
D. 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

Answer: B

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.

NEW QUESTION # 16
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

A. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
B. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)
C. UPDATE my_table SET v = VECTOR(4, FLOAT32)
D. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))

Answer: A

Explanation:
To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name "v", the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.

NEW QUESTION # 17
What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?

A. It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information
B. It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times but potentially increasing storage requirements
C. It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM
D. It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially requiring more development effort

Answer: C

Explanation:
RAG in Oracle AI Vector Search integrates vector search with LLMs, leveraging database-stored data. A key advantage is its use of existing database security and access controls (D), ensuring that sensitive enterprise data remains secure while being accessible to LLMs, aligning with Oracle's security model (e.g., roles, privileges). Performance optimization (A) occurs but isn't the primary focus; storage increases are minimal compared to security benefits. Real-time extraction (B) is possible but not RAG's core strength, which lies in static data augmentation. Training LLMs (C) is unrelated to RAG, which uses pre-trained models. Oracle emphasizes security integration as a standout RAG feature.

NEW QUESTION # 18
What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

A. To create vector indexes for efficient searches
B. To group vectors by their exact scores
C. To calculate the distance between vectors using a specified metric
D. To fetch rows that match exact vector embeddings

Answer: C

Explanation:
The VECTOR_DISTANCE function in Oracle 23ai (D) computes the distance between two vectors using a specified metric (e.g., COSINE, EUCLIDEAN), enabling similarity search by quantifying proximity. It doesn't fetch exact matches (A); it measures similarity. Index creation (B) is handled by CREATE INDEX, not this function. Grouping (C) requires additional SQL (e.g., GROUP BY), not VECTOR_DISTANCE's role. Oracle's SQL reference defines it as the core tool for distance calculation in vector queries.

NEW QUESTION # 19
If a query vector uses a different distance metric than the one used to create the index, whathappens?

A. An exact match search is triggered
B. The index automatically updates
C. A warning is logged, but the query executes
D. The query fails

Answer: D

Explanation:
In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine-based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index's structure. An exact match search (B) doesn't occur automatically; Oracle requires explicit control. The index doesn't update itself (C), and warnings (D) are not the default behavior-errors are raised instead. Oracle's documentation mandates metric consistency for index usage.

NEW QUESTION # 20
......

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