Oracle 1Z0-184-25 Exam Dumps

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.

SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type's expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn't supported for VECTOR columns, which require dense arrays. SQLLoader doesn't normalize vectors automatically (D); formatting must be explicit. Oracle's documentation specifies curly braces for CSV-loaded vectors.

To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name 'v', the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.

Approximate similarity search (ANN) in Oracle 23ai (B) uses indexes (e.g., HNSW, IVF) to trade accuracy for speed, returning near-matches faster by not comparing all vectors. Exact search compares every vector (A), not ANN. It doesn't guarantee 100% accuracy (C); that's exact search. It's faster, not slower (D), than exact search due to indexing. Oracle's documentation defines ANN's speed-accuracy trade-off as its hallmark.

Fetching the top-3 matching sentences requires a similarity search, and balancing speed and accuracy points to approximate nearest neighbor (ANN) techniques. Option A---approximate similarity search with VECTOR_DISTANCE---uses an index (e.g., HNSW, IVF) to quickly find near-matches, ordered by distance (e.g., SELECT sentence, VECTOR_DISTANCE(vector, :query_vector, COSINE) AS score FROM books ORDER BY score FETCH APPROXIMATE 3 ROWS ONLY). The APPROXIMATE clause leverages indexing for speed, with tunable accuracy (e.g., TARGET_ACCURACY), ideal for large datasets where exactness is traded for performance.

Option B (exact search with Euclidean) scans all vectors without indexing, ensuring 100% accuracy but sacrificing speed---impractical for big datasets. Option C (''multivector'' search) isn't a standard Oracle 23ai construct; it might imply multiple vectors per row, but lacks clarity and isn't optimal here. Option D (relational filters plus similarity) adds WHERE clauses (e.g., WHERE genre = 'fiction'), useful for scoping but not specified as needed, and doesn't inherently balance speed-accuracy without ANN. Oracle's ANN support in 23ai, via HNSW or IVF withVECTOR_DISTANCE, makes A the practical choice, aligning with real-world RAG use cases where response time matters as much as relevance.