PASS GUARANTEED QUIZ 2025 ORACLE 1Z0-184-25: ORACLE AI VECTOR SEARCH PROFESSIONAL NEWEST FREE PRACTICE

Pass Guaranteed Quiz 2025 Oracle 1Z0-184-25: Oracle AI Vector Search Professional Newest Free Practice

Pass Guaranteed Quiz 2025 Oracle 1Z0-184-25: Oracle AI Vector Search Professional Newest Free Practice

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Oracle AI Vector Search Professional Sample Questions (Q55-Q60):

NEW QUESTION # 55
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

  • A. To calculate vector distances
  • B. To serialize vectors into a string
  • C. To calculate vector dimensions
  • D. To generate a single vector embedding for data

Answer: D

Explanation:
The VECTOR_EMBEDDING function in Oracle 23ai (D) generates a vector embedding from input data (e.g., text) using a specified model (e.g., ONNX), producing a single VECTOR-type output for similarity search or AI tasks. It doesn't calculate dimensions (A); VECTOR_DIMENSION_COUNT does that. It doesn't compute distances (B); VECTOR_DISTANCE is for that. It doesn't serialize vectors (C); VECTOR_SERIALIZE handles serialization. Oracle's documentation positions VECTOR_EMBEDDING as the core function for in-database embedding creation, central to vector search workflows.


NEW QUESTION # 56
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

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

Answer: D


NEW QUESTION # 57
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

  • A. To calculate vector distances
  • B. To serialize vectors into a string
  • C. To calculate vector dimensions
  • D. To generate a single vector embedding for data

Answer: D


NEW QUESTION # 58
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. 1 MB
  • B. 256 KB
  • C. 4 MB
  • D. 1 GB

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 # 59
In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?
bash
CollapseWrapCopy
docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]
# Sample the resulting data
docs[:2]

  • A. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file
  • B. It improves the accuracy of the LLM by providing additional training data
  • C. It speeds up the vectorization process by providing a unique identifier for each chunk
  • D. It helps differentiate between chunks from different files but has no impact on vectorization

Answer: A

Explanation:
Prepending the filename to each text chunk (e.g., filename + "|" + section) in the Python code (A) preserves contextual metadata, linking each chunk-and its resulting vector-to its source file. This aids retrieval in RAG applications by allowing the application to trace back to the original document, enhancing response context (e.g., "from Book1"). While it differentiates chunks (B), its impact goes beyond identification, affecting retrieval usability. It doesn't speed up vectorization (C); embedding models process text regardless of prefixes. It also doesn't train the LLM (D); it's metadata for retrieval, not training data. Oracle's RAG examples emphasize metadata preservation for context-aware responses.


NEW QUESTION # 60
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