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NEW QUESTION # 38
You are tasked with finding the closest matching sentences across books, where each book has multiple paragraphs and sentences. Which SQL structure should you use?
Answer: A
Explanation:
Finding the closest matching sentences across books involves comparing a query vector to sentence vectors stored in a table (e.g., columns: book_id, sentence, vector). A nested query with ORDER BY (A) is the optimal SQL structure: an inner query computes distances (e.g., SELECT sentence, VECTOR_DISTANCE(vector, :query_vector, COSINE) AS score FROM sentences), and the outer query sorts and limits results (e.g., SELECT * FROM (inner_query) ORDER BY score FETCH FIRST 5 ROWS ONLY). This ranks sentences by similarity, leveraging Oracle's vector capabilities efficiently, especially with an index.
Option B (exact search) describes a technique, not a structure, and a full scan is slow without indexing-lacking specificity here. Option C (GROUP BY) aggregates (e.g., by book), not ranks individual sentences, missing the "closest" goal. Option D (FETCH PARTITIONS BY) isn't a valid clause; it might confuse with IVF partitioning, but that's index-related, not query syntax. The nested structure allows flexibility (e.g., adding WHERE clauses) and aligns with Oracle's vector search examples, ensuring both correctness and scalability-crucial when books yield thousands of sentences.
NEW QUESTION # 39
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?
Answer: D
Explanation:
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.
NEW QUESTION # 40
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?
Answer: D
Explanation:
The VECTOR_EMBEDDING function in Oracle Database 23ai generates a vector embedding from input data (e.g., a text string) using a specified model, such as an ONNX model loaded into the database. It's designed for in-database embedding creation, supporting vector search and AI applications. Options A, B, and C (GENERATE_EMBEDDING, CREATE_VECTOR_EMBEDDING, EMBED_TEXT) are not valid SQL functions in 23ai. VECTOR_EMBEDDING integrates seamlessly with the VECTOR data type and is documented as the standard method for embedding generation in SQL queries.
NEW QUESTION # 41
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?
Answer: B
Explanation:
The VECTOR_DISTANCE function in Oracle 23ai computes vector distances, and if no metric is specified (e.g., VECTOR_DISTANCE(v1, v2)), it defaults to Cosine (C). Cosine distance (1 - cosine similarity) is widely used for text embeddings due to its focus on angular separation, ignoring magnitude-fitting for normalized vectors from models like BERT. Euclidean (A) measures straight-line distance, not default. Hamming (B) is for binary vectors, rare in 23ai's FLOAT32 context. Manhattan (D) sums absolute differences, less common for embeddings. Oracle's choice of Cosine reflects its AI focus, as documentation confirms, aligning with industry norms for semantic similarity-vital for users assuming defaults in queries.
NEW QUESTION # 42
What is the significance of using local ONNX models for embedding within the database?
Answer: B
Explanation:
Using local ONNX (Open Neural Network Exchange) models for embedding within Oracle Database 23ai means loading pre-trained models (e.g., via DBMS_VECTOR) into the database to generate vectors internally, rather than relying on external APIs or services. The primary significance is enhanced security (D): sensitive data (e.g., proprietary documents) never leaves the database, avoiding exposure to external networks or third-party providers. This aligns with enterprise needs for data privacy and compliance (e.g., GDPR), as the embedding process-say, converting "confidential report" to a vector-occurs within Oracle's secure environment, leveraging its encryption and access controls.
Option A (SQLPlus support) is irrelevant; ONNX integration is about AI functionality, not legacy client compatibility-SQLPlus can query vectors regardless. Option B (improved accuracy) is misleading; accuracy depends on the model's training, not its location-local vs. external models could be identical (e.g., same BERT variant). Option C (reduced dimensions) is a misconception; dimensionality is model-defined (e.g., 768 for BERT), not altered by locality-processing speed might improve due to reduced latency, but that's secondary. Security is the standout benefit, as Oracle's documentation emphasizes in-database processing to minimize data egress risks, a critical consideration for RAG or Select AI workflows where private data fuels LLMs. Without this, external calls could leak context, undermining trust in AI applications.
NEW QUESTION # 43
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