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Oracle AI Vector Search Professional Sample Questions (Q25-Q30):

NEW QUESTION # 25
What is the purpose of the Vector Pool in Oracle Database 23ai?

A. To enable longer SQL execution
B. To manage database partitioning
C. To store HNSW vector indexes and IVF index metadata
D. To store non-vector data types

Answer: C

Explanation:
The Vector Pool in Oracle 23ai is a dedicated SGA memory region (controlled by VECTOR_MEMORY_SIZE) for vector operations, specifically storing HNSW indexes (graph structures) and IVF index metadata (e.g., centroids) (B). This optimizes memory usage for vector search, keeping critical index data accessible for fast queries. Partitioning (A) is unrelated; that's a tablespace feature. Longer SQL execution (C) might benefit indirectly from memory efficiency, but it's not the purpose. Non-vector data (D) resides elsewhere (e.g., PGA, buffer cache). Oracle allocates the Vector Pool to enhance AI workloads, ensuring indexes don't compete with other memory, a design choice reflecting vector search's growing importance.

NEW QUESTION # 26
You are tasked with creating a table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions, and the dimensions should be stored as 32-bitfloating point numbers. Which SQL statement should you use?

A. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))
B. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))
C. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))
D. CREATE TABLE vectors (id NUMBER, embedding VECTOR)

Answer: C

Explanation:
In Oracle 23ai, the VECTOR data type can specify dimensions and precision. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32)) (D) defines a column with exactly 512 dimensions and FLOAT32 (32-bit float) format, meeting both requirements. Option A omits the format (defaults vary), risking mismatch. Option B is unspecified, allowing variable dimensions-not "exactly 512." Option C uses INT8, not FLOAT32, and '*' denotes undefined dimensions. Oracle's SQL reference confirms this syntax for precise VECTOR definitions.

NEW QUESTION # 27
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

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

Answer: C

Explanation:
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.

NEW QUESTION # 28
What is the primary purpose of the DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS package in a RAG application?

A. To generate vector embeddings from a text document
B. To split a large document into smaller chunks to improve vector quality by minimizing token truncation
C. To load a document into the database
D. To convert a document into a single, large text string

Answer: B

Explanation:
In Oracle Database 23ai, the DBMS_VECTOR_CHAIN package supports Retrieval Augmented Generation (RAG) workflows by providing utilities for vector processing. The UTL_TO_CHUNKS function specifically splits large documents into smaller, manageable text chunks. This is critical in RAG applications because embedding models (e.g., BERT, ONNX models) have token limits (e.g., 512 tokens). Splitting text minimizes token truncation, ensuring that each chunk retains full semantic meaning, which improves the quality of subsequent vector embeddings and search accuracy. Generating embeddings (A) is handled by functions like VECTOR_EMBEDDING, not UTL_TO_CHUNKS. Loading documents (B) is a separate process (e.g., via SQL*Loader). Converting to a single text string (D) contradicts the chunking purpose and risks truncation. Oracle's documentation on DBMS_VECTOR_CHAIN emphasizes chunking for optimizing vector quality in RAG.

NEW QUESTION # 29
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

A. Multivector similarity search with partitioning
B. Approximate similarity search with IVF indexing and target accuracy of 70%
C. Exact similarity search using a full table scan
D. Approximate similarity search with HNSW indexing and target accuracy of 70%

Answer: C

Explanation:
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.

NEW QUESTION # 30
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