LOOKING TO ADVANCE YOUR ORACLE CAREER? TRY ORACLE 1Z0-184-25 EXAM QUESTIONS

Looking to Advance Your Oracle Career? Try Oracle 1Z0-184-25 Exam Questions

Looking to Advance Your Oracle Career? Try Oracle 1Z0-184-25 Exam Questions

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Oracle AI Vector Search Professional Sample Questions (Q47-Q52):

NEW QUESTION # 47
What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?

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

Answer: B

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 # 48
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

  • A. Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search
  • B. Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types
  • C. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
  • D. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database

Answer: C

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.


NEW QUESTION # 49
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?

  • A. 256 KB
  • B. 1 GB
  • C. 4 MB
  • D. 1 MB

Answer: C

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 # 50
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

  • A. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
  • B. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
  • C. Both operate identically but differ in memory usage
  • D. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy

Answer: B


NEW QUESTION # 51
Which is a characteristic of an approximate similarity search in Oracle Database 23ai?

  • A. It trades off accuracy for faster performance
  • B. It always guarantees 100% accuracy
  • C. It is slower than exact similarity search
  • D. It compares every vector in the dataset

Answer: A

Explanation:
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.


NEW QUESTION # 52
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