Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with

Generative AI services, such as OCI Generative AI, enabling features like natural language query

processing (e.g., Select AI) and AI-driven insights. DBMS_ML (B) focuses on machine learning model

training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing

workflows (e.g., document chunking, embedding), but it’s not the main interface for generative AI

services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI’s role is

highlighted in Oracle’s AI integration features for 23ai.

Reference:Oracle Database 23ai New Features Guide, Chapter on AI Integration.

Exadata in Oracle Database 23ai enhances AI and vector search capabilities. Vector Replication with

GoldenGate (B) supports real-time vector data distribution. SQL*Loader (C) loads vector data into

VECTOR columns. AI Smart Scan (D) accelerates AI workloads using Exadata’s storage optimizations.

However, “Native Support for Vector Search Only within the Database Server” (A) is not a feature;

vector search is natively supported across Exadata’s architecture, leveraging both database and

storage layers (e.g., via Smart Scan), not restricted to the server alone. This option misrepresents

Exadata’s distributed capabilities, making it the correct “NOT” answer.

Reference:Oracle Database 23ai Exadata System Software Guide, Chapter on AI Features.

Building a RAG application in Oracle 23ai using PL/SQL follows a logical sequence: (1) Load Document

(e.g., via SQL*Loader) into the database; (2) Split Text into Chunks (e.g.,

DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) to manage token limits; (3) Load ONNX Model (e.g., via

DBMS_VECTOR) for embedding generation; (4) Create Embeddings (e.g., UTL_TO_EMBEDDINGS) for

the chunks; (5) Vectorize Question (using the same model) when a query is received; (6) Perform

Vector Search (e.g., VECTOR_DISTANCE) to find relevant chunks; (7) Generate Output (e.g., via

DBMS_AI with an LLM). Option B matches this flow. A starts with the model prematurely. C prioritizes

the question incorrectly. D is close but loads the model too early. Oracle’s RAG workflow

documentation outlines this document-first approach.

Reference:Oracle Database 23ai AI Vector Search Guide, Chapter on RAG Workflow.

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.

Reference:Oracle Database 23ai AI Vector Search Guide, Section on Vector Index Metrics.

Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed

and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high

recall—ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for

scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW’s accuracy,

trading off recall for memory efficiency. BT (A) isn’t a 23ai vector index; it’s a generic term unrelated

here. IFS (B) seems a typo for IVF; no such index exists. HNSW’s graph structure outperforms IVF in

small-to-medium datasets or where precision matters, as Oracle’s documentation and benchmarks

highlight, making it a go-to for balanced performance.

Reference:Oracle Database 23ai AI Vector Search Guide, Section on HNSW Indexing.