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Oracle AI Vector Search Professional Sample Questions (Q45-Q50):
NEW QUESTION # 45
What is the primary function of AI Smart Scan in Exadata System Software 24ai?
- A. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing
- B. To automatically optimize database queries for improved performance
- C. To provide real-time monitoring and diagnostics for AI applications
Answer: A
Explanation:
AI Smart Scan in Exadata System Software 24ai (B) accelerates AI workloads, including vector search, by offloading processing to storage servers using Exadata's RDMA Memory (XRMEM), Smart Cache, and on-storage capabilities. This enhances performance for large-scale vector operations. Real-time monitoring (A) isn't its focus; that's for management tools. Queryoptimization (C) is a general Exadata feature (Smart Scan), but AI Smart Scan specifically targets AI tasks. Oracle's 24ai documentation emphasizes its role in speeding up AI computations.
NEW QUESTION # 46
A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?
- A. Increase the VECTOR_MEMORY_SIZE initialization parameter
- B. Re-create the index with a higher EFCONSTRUCTION value
- C. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
- D. Change the index type to HNSW for better accuracy
Answer: C
Explanation:
IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn't directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle's IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.
NEW QUESTION # 47
Which operation is NOT permitted on tables containing VECTOR columns?
- A. UPDATE
- B. DELETE
- C. JOIN ON VECTOR columns
- D. SELECT
Answer: C
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 # 48
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?
- A. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
- B. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
- C. By requiring users to manually enter their OCI API keys each time they execute a natural language query
- D. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
Answer: B
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 # 49
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?
- A. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
- B. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
- C. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, 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: B
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 # 50
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