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Oracle AI Vector Search Professional Sample Questions (Q33-Q38):
NEW QUESTION # 33
What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?
- A. To fetch rows that match exact vector embeddings
- B. To create vector indexes for efficient searches
- C. To group vectors by their exact scores
- D. To calculate the distance between vectors using a specified metric
Answer: D
Explanation:
The VECTOR_DISTANCE function in Oracle 23ai (D) computes the distance between two vectors using a specified metric (e.g., COSINE, EUCLIDEAN), enabling similarity search by quantifying proximity. It doesn't fetch exact matches (A); it measures similarity. Index creation (B) is handled by CREATE INDEX, not this function. Grouping (C) requires additional SQL (e.g., GROUP BY), not VECTOR_DISTANCE's role. Oracle's SQL reference defines it as the core tool for distance calculation in vector queries.
NEW QUESTION # 34
When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?
- A. The physical location where the vector embeddings are stored
- B. The choice of programming language used to process the dataset (e.g., Python, Java)
- C. The storage format of the new dataset (e.g., CSV, JSON)
- D. The same vector embedding model must be used for vectorizing the data and creating a query vector
Answer: D
Explanation:
Meaningful similarity search relies on the consistency of the vector space in which embeddings reside. Vector embeddings are generated by models (e.g., BERT, SentenceTransformer) that map data into a high-dimensional space, where proximity reflects semantic similarity. If different models are used for the dataset and query vector, the embeddings will be in incompatible spaces, rendering distance metrics (e.g., cosine, Euclidean) unreliable. The programming language (A) affects implementation but not the semantic consistency of embeddings-Python or Java can use the same model equally well. The physical storage location (B) impacts accessibility and latency but not the mathematical validity of similarity comparisons. The storage format (C) influences parsing andingestion but does not determine the embedding space. Oracle 23ai's vector search framework explicitly requires the same embedding model for data and queries to ensure accurate results, a principle that applies universally, even outside the database.
NEW QUESTION # 35
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?
- A. OCI Data Science
- B. OCI Generative AI
- C. OCI Language
- D. OCI Vision
Answer: B
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 # 36
What is the primary purpose of the DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS package in a RAG application?
- A. To split a large document into smaller chunks to improve vector quality by minimizing token truncation
- B. To load a document into the database
- C. To generate vector embeddings from a text document
- D. To convert a document into a single, large text string
Answer: A
Explanation:
In Oracle Database 23ai, the DBMS_VECTOR_CHAIN package supports Retrieval Augmented Generation (RAG) workflows by providing utilities for vector processing. The UTL_TO_CHUNKS function specifically splits large documents into smaller, manageable text chunks. This is critical in RAG applications because embedding models (e.g., BERT, ONNX models) have token limits (e.g., 512 tokens). Splitting text minimizes token truncation, ensuring that each chunk retains full semantic meaning, which improves the quality of subsequent vector embeddings and search accuracy. Generating embeddings (A) is handled by functions like VECTOR_EMBEDDING, not UTL_TO_CHUNKS. Loading documents (B) is a separate process (e.g., via SQL*Loader). Converting to a single text string (D) contradicts the chunking purpose and risks truncation. Oracle's documentation on DBMS_VECTOR_CHAIN emphasizes chunking for optimizing vector quality in RAG.
NEW QUESTION # 37
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 GB
- B. 1 MB
- C. 4 MB
- D. 256 KB
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 # 38
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