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Oracle AI Vector Search Professional Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

A. OCI Data Science
B. OCI Vision
C. OCI Generative AI
D. OCI Language

Answer: C

Explanation:
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.

NEW QUESTION # 31
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

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

Answer: A

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 # 32
Which function should you use to determine the storage format of a vector?

A. VECTOR_CHUNKS
B. VECTOR_NORM
C. VECTOR_EMBEDDING
D. VECTOR_DIMENSION_FORMAT

Answer: D

NEW QUESTION # 33
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

A. Use UNION ALL with vector operations
B. Apply relational filters and a similarity search in the query
C. Use VECTOR_INDEX_HINT and NO WHERE clause
D. Perform the similarity search without a WHERE clause

Answer: B

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.

NEW QUESTION # 34
When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

A. In a CSV file
B. In a dedicated vector database
C. In the database as BLOB (Binary Large Object) data
D. In a binary FVEC file with the relational data in a CSV file

Answer: B

Explanation:
When vector embeddings are generated outside the database, the storage choice must balance efficiency, scalability, and usability for similarity search. A CSV file (A) is simple and human-readable but inefficient for large-scale vector operations due to text parsing overhead and lack of indexing support. A binary FVEC file (B) offers a compact format for vectors, reducing storage size and improving read performance, but separating relational data into a CSV complicates integration and querying, making it suboptimal for unified workflows. Storing embeddings as BLOBs in a relational database (C) integrates well with structured data and supports SQL access, but it lacks the specialized indexing (e.g., HNSW, IVF) and query optimizations that dedicated vector databases provide. A dedicated vector database (D), such as Milvus or Pinecone (or Oracle 23ai's vector capabilities if internal), is purpose-built for high-dimensional vectors, offering efficient storage, advanced indexing, and fast approximate nearest neighbor (ANN) searches. For external generation scenarios, where embeddings are not immediately integrated into Oracle 23ai, a dedicated vector database is the most suitable due to its performance and scalability advantages. Oracle's AI Vector Search documentation indirectly supports this by emphasizing optimized vector storage for search efficiency, though it focuses on in-database solutions.

NEW QUESTION # 35
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1Z0-184-25 New Test Materials, 1Z0-184-25 Exam Vce Format

1Z0-184-25 Exam Vce Format, 1Z0-184-25 Reliable Test Voucher

Oracle AI Vector Search Professional Sample Questions (Q30-Q35):

Quiz Oracle - Authoritative 1Z0-184-25 - Oracle AI Vector Search Professional New Test Materials

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