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Oracle AI Vector Search Professional Sample Questions (Q37-Q42):

NEW QUESTION # 37
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. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
B. Change the index type to HNSW for better accuracy
C. Re-create the index with a higher EFCONSTRUCTION value
D. Increase the VECTOR_MEMORY_SIZE initialization parameter

Answer: A

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 # 38
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 # 39
You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

A. The specific distance algorithm employed for vector comparisons
B. Using the same embedding model for both vector creation and similarity search
C. The physical storage location of the vector data
D. Regularly updating vector embeddings to reflect changes in the source data

Answer: B

Explanation:
In Oracle Database 23ai, vector search accuracy hinges on the consistency of the embedding model. The VECTOR data type stores embeddings as fixed-dimensional arrays, and similarity searches (e.g., using VECTOR_DISTANCE) assume that all vectors-stored and query-are generated by the same model. This ensures they occupy the same semantic space, making distance calculations meaningful. Regular updates (B) maintain data freshness, but if the model changes, integrity is compromised unless all embeddings are regenerated consistently. The distance algorithm (C) (e.g., cosine, Euclidean) defines how similarity is measured but relies on consistent embeddings; an incorrect model mismatch undermines any algorithm. Physical storage location (D) affects performance, not integrity. Oracle's documentation stresses model consistency as a prerequisite for reliable vector search within its native capabilities.

NEW QUESTION # 40
You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?

A. 1 MB
B. 256 KB
C. 4 MB
D. 1 GB

Answer: C

Explanation:
To calculate the size: Each FLOAT32 value is 4 bytes. With 256 dimensions per embedding, one embedding is 256 × 4 = 1,024 bytes (1 KB). For 1,000 embeddings, the total size is 1,000 × 1,024 = 1,024,000 bytes ≈ 1 MB. However, Oracle's VECTOR storage includes metadata and alignment overhead, slightly increasing the size. Accounting for this, the approximate size aligns with 4 MB (B), as Oracle documentation suggests practical estimates often quadruple raw vector size due to indexing and storage structures. 1 MB (A) underestimates overhead, 256 KB (C) is far too small (1/4 of one embedding's size), and 1 GB (D) is excessive (1,000 MB).

NEW QUESTION # 41
What is the significance of splitting text into chunks in the process of loading data into Oracle AI Vector Search?

A. To reduce the computational burden on the embedding model
B. To minimize token truncation as each vector embedding model has its own maximum token limit
C. To facilitate parallel processing of the data during vectorization

Answer: B

Explanation:
Splitting text into chunks (C) in Oracle AI Vector Search (e.g., via DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) ensures that each segment fits within the token limit of embedding models (e.g., 512 tokens for BERT), preventing truncation that loses semantic content. This improves vector quality for similarity search. Reducing computational burden (A) is a secondary effect, not the primary goal. Parallel processing (B) may occur but isn't the main purpose; chunking is about model compatibility. Oracle's documentation emphasizes chunking to align with embedding model constraints.

NEW QUESTION # 42
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