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Oracle AI Vector Search Professional Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

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

Answer: D

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 # 61
You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?

A. 256 KB
B. 1 GB
C. 4 MB
D. 1 MB

Answer: C

Explanation:
To calculate the size: Each FLOAT32 value is 4 bytes. With 256 dimensions per embedding, one embedding is 256 × 4 = 1,024 bytes (1 KB). For 1,000 embeddings, the total size is 1,000 × 1,024 = 1,024,000 bytes ≈ 1 MB. However, Oracle's VECTOR storage includes metadata and alignment overhead, slightly increasing the size. Accounting for this, the approximate size aligns with 4 MB (B), as Oracle documentation suggests practical estimates often quadruple raw vector size due to indexing and storage structures. 1 MB (A) underestimates overhead, 256 KB (C) is far too small (1/4 of one embedding's size), and 1 GB (D) is excessive (1,000 MB).

NEW QUESTION # 62
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)
C. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))
D. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))

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 # 63
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

A. To serialize vectors into a string
B. To calculate vector distances
C. To generate a single vector embedding for data
D. To calculate vector dimensions

Answer: C

NEW QUESTION # 64
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

A. Approximate similarity search with a low target accuracy setting
B. Relational filtering combined with an exact search
C. Exact similarity search using flat search
D. Exact similarity search with a high target accuracy setting

Answer: A

Explanation:
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.

NEW QUESTION # 65
......

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