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Oracle AI Vector Search Professional Sample Questions (Q18-Q23):

NEW QUESTION # 18
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

A. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
B. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
C. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
D. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output

Answer: C

Explanation:
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.

NEW QUESTION # 19
Which operation is NOT permitted on tables containing VECTOR columns?

A. JOIN ON VECTOR columns
B. DELETE
C. UPDATE
D. SELECT

Answer: A

Explanation:
In Oracle 23ai, tables with VECTOR columns support standard DML operations: SELECT (A) retrieves data, UPDATE (B) modifies rows, and DELETE (C) removes rows. However, JOIN ON VECTOR columns (D) is not permitted because VECTOR isn't a relational type for equality comparison; it's for similarity search (e.g., via VECTOR_DISTANCE). Joins must use non-VECTOR columns. Oracle's SQL reference restricts VECTOR to specific operations, excluding direct joins.

NEW QUESTION # 20
In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?
bash
CollapseWrapCopy
docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]
# Sample the resulting data
docs[:2]

A. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file
B. It helps differentiate between chunks from different files but has no impact on vectorization
C. It improves the accuracy of the LLM by providing additional training data
D. It speeds up the vectorization process by providing a unique identifier for each chunk

Answer: A

Explanation:
Prepending the filename to each text chunk (e.g., filename + "|" + section) in the Python code (A) preserves contextual metadata, linking each chunk-and its resulting vector-to its source file. This aids retrieval in RAG applications by allowing the application to trace back to the original document, enhancing response context (e.g., "from Book1"). While it differentiates chunks (B), its impact goes beyond identification, affecting retrieval usability. It doesn't speed up vectorization (C); embedding models process text regardless of prefixes. It also doesn't train the LLM (D); it's metadata for retrieval, not training data. Oracle's RAG examples emphasize metadata preservation for context-aware responses.

NEW QUESTION # 21
What is the significance of using local ONNX models for embedding within the database?

A. Enhanced security because data remains within the database
B. Support for legacy SQL*Plus clients
C. Improved accuracy compared to external models
D. Reduced embedding dimensions for faster processing

Answer: A

Explanation:
Using local ONNX (Open Neural Network Exchange) models for embedding within Oracle Database 23ai means loading pre-trained models (e.g., via DBMS_VECTOR) into the database to generate vectors internally, rather than relying on external APIs or services. The primary significance is enhanced security (D): sensitive data (e.g., proprietary documents) never leaves the database, avoiding exposure to external networks or third-party providers. This aligns with enterprise needs for data privacy and compliance (e.g., GDPR), as the embedding process-say, converting "confidential report" to a vector-occurs within Oracle's secure environment, leveraging its encryption and access controls.
Option A (SQLPlus support) is irrelevant; ONNX integration is about AI functionality, not legacy client compatibility-SQLPlus can query vectors regardless. Option B (improved accuracy) is misleading; accuracy depends on the model's training, not its location-local vs. external models could be identical (e.g., same BERT variant). Option C (reduced dimensions) is a misconception; dimensionality is model-defined (e.g., 768 for BERT), not altered by locality-processing speed might improve due to reduced latency, but that's secondary. Security is the standout benefit, as Oracle's documentation emphasizes in-database processing to minimize data egress risks, a critical consideration for RAG or Select AI workflows where private data fuels LLMs. Without this, external calls could leak context, undermining trust in AI applications.

NEW QUESTION # 22
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 dimensions
C. To calculate vector distances
D. To generate a single vector embedding for data

Answer: D

NEW QUESTION # 23
......

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