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Oracle AI Vector Search Professional Sample Questions (Q24-Q29):

NEW QUESTION # 24
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

A. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
B. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
C. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
D. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output

Answer: A

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 # 25
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"""

A. It filters out vectors with a cosine similarity below a certain threshold
B. It converts the vectors to a format compatible with the SQL database
C. It indicates that the cosine distance metric should be used to measure similarity between vectors
D. It specifies the type of vector encoding used in the database

Answer: C

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 # 26
What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?

A. Real-time vector data updates across locations
B. Built-in version control for vector data
C. Specialized vector embedding compression
D. Automatic translation of vector embeddings between formats

Answer: A

Explanation:
Oracle GoldenGate 23ai is a real-time data replication and integration tool, extended in 23ai to handle the VECTOR data type for AI applications. Its key advantage (A) is enabling real-time updates of vector data across distributed locations-e.g., replicating VECTOR columns from a primary database in New York to a secondary in London with sub-second latency. This ensures AI models (e.g., for similarity search or RAG) access the latest embeddings as source data (e.g., documents) changes, critical for dynamic environments like customer support systems where new queries demand current context. Imagine a VECTOR column storing embeddings of support tickets; GoldenGate keeps these synchronized across regions, minimizing staleness that could degrade AI responses.
Option B (automatic translation) is fictional; GoldenGate doesn't convert vector formats (e.g., FLOAT32 to INT8)-that's a model or application task. Option C (compression) isn't a GoldenGate feature; compression might occur at the storage layer, but GoldenGate focuses on replication fidelity, not size reduction. Option D (version control) misaligns with GoldenGate's purpose; it ensures data consistency, not historical versioning like Git. Real-time replication (A) stands out, as Oracle's documentation emphasizes GoldenGate's role in keeping vector-driven AI applications globally consistent, a game-changer for distributed AI deployments where latency or inconsistency could disrupt user trust. Without this, static exports (e.g., Data Pump) would lag, undermining real-time AI use cases.

NEW QUESTION # 27
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

A. It guarantees higher accuracy than Euclidean Distance
B. It is simpler and faster because it avoids square-root calculations
C. It is the default distance metric for Oracle AI Vector Search
D. It supports hierarchical partitioning of vectors

Answer: B

Explanation:
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster-crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.

NEW QUESTION # 28
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 # 29
......

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