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Oracle AI Vector Search Professional Sample Questions (Q38-Q43):
NEW QUESTION # 38
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?
Answer: D
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 # 39
Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?
Answer: A
Explanation:
Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with Generative AI services, such as OCI Generative AI, enabling features like natural language query processing (e.g., Select AI) and AI-driven insights. DBMS_ML (B) focuses on machine learning model training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing workflows (e.g., document chunking, embedding), but it's not the main interface for generative AI services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI's role is highlighted in Oracle's AI integration features for 23ai.
NEW QUESTION # 40
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?
Answer: D
NEW QUESTION # 41
What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?
topk = 3
sql = f"""select payload, vector_distance(vector, :vector, COSINE) as score from {table_name} order by score fetch approximate {topk} rows only"""
Answer: A
Explanation:
In Oracle Database 23ai, the VECTOR_DISTANCE function calculates the distance between two vectors using a specified metric. The COSINE parameter in the query (vector_distance(vector, :vector, COSINE)) instructs the database to use the cosine distance metric (C) to measure similarity. Cosine distance, defined as 1 - cosine similarity, is ideal for high-dimensional vectors (e.g., text embeddings) as it focuses on angular separation rather than magnitude. It doesn't filter vectors (A); filtering requires additional conditions (e.g., WHERE clause). It doesn't convert vector formats (B); vectors are already in the VECTOR type. It also doesn't specify encoding (D), which is defined during vector creation (e.g., FLOAT32). Oracle's documentation confirms COSINE as one of the supported metrics for similarity search.
NEW QUESTION # 42
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?
Answer: D
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 # 43
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