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100% Pass Oracle - High-quality 1Z0-184-25 - Oracle AI Vector Search Professional Guaranteed Questions Answers

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Oracle AI Vector Search Professional Sample Questions (Q23-Q28):

NEW QUESTION # 23
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

A. VECTOR_EMBEDDING
B. CREATE_VECTOR_EMBEDDING
C. EMBED_TEXT
D. GENERATE_EMBEDDING

Answer: A

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 # 24
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

A. By requiring users to manually enter their OCI API keys each time they execute a natural language query
B. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
C. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
D. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service

Answer: C

Explanation:
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.

NEW QUESTION # 25
Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23ai?

A. Adding a new VECTOR column to the table
B. Modifying the data type of an existing VECTOR column to a non-VECTOR type
C. Dropping an existing VECTOR column from the table
D. Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table

Answer: B

Explanation:
Oracle Database 23ai imposes restrictions on DDL operations for tables with VECTOR columns to preserve data integrity. CTAS (A) is permitted, as it copies the VECTOR column intact into a new table, maintaining its structure. Dropping a VECTOR column (B) is allowed via ALTER TABLE DROP COLUMN, as it simply removes the column without altering its type. Adding a new VECTOR column (D) is supported with ALTER TABLE ADD, enabling schema evolution. However, modifying an existing VECTOR column's data type to a non-VECTOR type (C) (e.g., VARCHAR2, NUMBER) is not permitted because VECTOR is a specialized type with dimensional and format constraints (e.g., FLOAT32), and Oracle does not support direct type conversion due to potential loss of semantic meaning and structure. This restriction is documented in Oracle's SQL reference.

NEW QUESTION # 26
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 choice of programming language used to process the dataset (e.g., Python, Java)
B. The same vector embedding model must be used for vectorizing the data and creating a query vector
C. The physical location where the vector embeddings are stored
D. The storage format of the new dataset (e.g., CSV, JSON)

Answer: B

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 # 27
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

A. Perform the similarity search without a WHERE clause
B. Use UNION ALL with vector operations
C. Use VECTOR_INDEX_HINT and NO WHERE clause
D. Apply relational filters and a similarity search in the query

Answer: D

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.

NEW QUESTION # 28
......

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