LATEST 1Z0-184-25 EXAM LABS - FREE PDF ORACLE FIRST-GRADE VALID 1Z0-184-25 TEST VCE

Latest 1Z0-184-25 Exam Labs - Free PDF Oracle First-grade Valid 1Z0-184-25 Test Vce

Latest 1Z0-184-25 Exam Labs - Free PDF Oracle First-grade Valid 1Z0-184-25 Test Vce

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Oracle AI Vector Search Professional Sample Questions (Q58-Q63):

NEW QUESTION # 58
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 helps differentiate between chunks from different files but has no impact on vectorization
  • D. It speeds up the vectorization process by providing a unique identifier for each chunk

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 # 59
Why would you choose to NOT define a specific size for the VECTOR column during development?

  • A. It impacts the accuracy of similarity searches
  • B. It limits the length of text that can be vectorized
  • C. It restricts the database to a single embedding model
  • D. Different external embedding models produce vectors with varying dimensions and data types

Answer: D

Explanation:
In Oracle Database 23ai, a VECTOR column can be defined with a specific size (e.g., VECTOR(512, FLOAT32)) or left unspecified (e.g., VECTOR). Not defining a size (D) provides flexibility during development because different embedding models (e.g., BERT, SentenceTransformer) generate vectors with varying dimensions (e.g., 768, 384) and data types (e.g., FLOAT32, INT8). This avoids locking the schema into one model, allowing experimentation. Accuracy (A) isn't directly impacted by size definition; it depends on the model and metric. A fixed size doesn't restrict the database to one model (B) but requires matching dimensions. Text length (C) affects tokenization, not vector dimensions. Oracle's documentation supports undefined VECTOR columns for flexibility in AI workflows.


NEW QUESTION # 60
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

  • A. OCI Language
  • B. OCI Data Science
  • C. OCI Vision
  • D. OCI Generative AI

Answer: D

Explanation:
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.


NEW QUESTION # 61
Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?

  • A. DBMS_GENAI
  • B. DBMS_AI
  • C. DBMS_ML
  • D. DBMS_VECTOR_CHAIN

Answer: B

Explanation:
Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with Generative AI services, such as OCI Generative AI, enabling features like natural language query processing (e.g., Select AI) and AI-driven insights. DBMS_ML (B) focuses on machine learning model training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing workflows (e.g., document chunking, embedding), but it's not the main interface for generative AI services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI's role is highlighted in Oracle's AI integration features for 23ai.


NEW QUESTION # 62
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

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

Answer: B


NEW QUESTION # 63
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