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Oracle AI Vector Search Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

A. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
B. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, 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. Load ONNX Model, Vectorize Question, 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 # 13
What is a key characteristic of HNSW vector indexes?

A. They use hash-based clustering
B. They are disk-based structures
C. They require exact match for searches
D. They are hierarchical with multilayered connections

Answer: D

Explanation:
HNSW (Hierarchical Navigable Small World) indexes in Oracle 23ai (A) are characterized by a hierarchical structure with multilayered connections, enabling efficient approximate nearest neighbor (ANN) searches. This graph-based approach connects vectors across levels, balancing speed and accuracy. They don't require exact matches (B); they're designed for approximate searches. They're memory-optimized, not solely disk-based (C), though persisted to disk. Hash-based clustering (D) relates to other methods (e.g., LSH), not HNSW. Oracle's documentation highlights HNSW's hierarchical nature as key to its performance.

NEW QUESTION # 14
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

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

Answer: B

Explanation:
The VECTOR_EMBEDDING function in Oracle 23ai (D) generates a vector embedding from input data (e.g., text) using a specified model (e.g., ONNX), producing a single VECTOR-type output for similarity search or AI tasks. It doesn't calculate dimensions (A); VECTOR_DIMENSION_COUNT does that. It doesn't compute distances (B); VECTOR_DISTANCE is for that. It doesn't serialize vectors (C); VECTOR_SERIALIZE handles serialization. Oracle's documentation positions VECTOR_EMBEDDING as the core function for in-database embedding creation, central to vector search workflows.

NEW QUESTION # 15
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

A. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
B. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
C. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
D. By requiring users to manually enter their OCI API keys each time they execute a natural language query

Answer: C

Explanation:
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.

NEW QUESTION # 16
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

A. A secure VPN tunnel
B. A new user account with elevated privileges
C. An AI profile for OCI Generative AI
D. A dedicated OCI compartment

Answer: C

Explanation:
To integrate OCI Generative AI with Autonomous Database in Oracle 23ai (e.g., for Select AI), an AI profile (A) is created within the database using DBMS_AI. This profile configures the connection to OCI Generative AI, specifying the LLM and authentication (e.g., Resource Principals). A compartment (B) organizes OCI resources but isn't "created" specifically for this integration; it's a prerequisite. A new user account (C) or VPN tunnel (D) isn't required; security leverages existing mechanisms. Oracle's Select AI setup documentation highlights the AI profile as the key facilitator.

NEW QUESTION # 17
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