TOP VALID EXAM 1Z0-184-25 PREPARATION & THE BEST SITE PREP4SUREEXAM TO HELP YOU PASS 1Z0-184-25: ORACLE AI VECTOR SEARCH PROFESSIONAL

Top Valid Exam 1Z0-184-25 Preparation & The Best Site Prep4sureExam to help you pass 1Z0-184-25: Oracle AI Vector Search Professional

Top Valid Exam 1Z0-184-25 Preparation & The Best Site Prep4sureExam to help you pass 1Z0-184-25: Oracle AI Vector Search Professional

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.
Topic 2
  • Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.
Topic 3
  • Leveraging Related AI Capabilities: This section evaluates the skills of Cloud AI Engineers in utilizing Oracle’s AI-enhanced capabilities. It covers the use of Exadata AI Storage for faster vector search, Select AI with Autonomous for querying data using natural language, and data loading techniques using SQL Loader and Oracle Data Pump to streamline AI-driven workflows.

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Oracle AI Vector Search Professional Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

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

Answer: C

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 # 27
A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?

  • A. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
  • B. Re-create the index with a higher EFCONSTRUCTION value
  • C. Change the index type to HNSW for better accuracy
  • D. Increase the VECTOR_MEMORY_SIZE initialization parameter

Answer: A

Explanation:
IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn't directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle's IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.


NEW QUESTION # 28
What is the primary purpose of the DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS package in a RAG application?

  • A. To generate vector embeddings from a text document
  • B. To convert a document into a single, large text string
  • C. To load a document into the database
  • D. To split a large document into smaller chunks to improve vector quality by minimizing token truncation

Answer: D

Explanation:
In Oracle Database 23ai, the DBMS_VECTOR_CHAIN package supports Retrieval Augmented Generation (RAG) workflows by providing utilities for vector processing. The UTL_TO_CHUNKS function specifically splits large documents into smaller, manageable text chunks. This is critical in RAG applications because embedding models (e.g., BERT, ONNX models) have token limits (e.g., 512 tokens). Splitting text minimizes token truncation, ensuring that each chunk retains full semantic meaning, which improves the quality of subsequent vector embeddings and search accuracy. Generating embeddings (A) is handled by functions like VECTOR_EMBEDDING, not UTL_TO_CHUNKS. Loading documents (B) is a separate process (e.g., via SQL*Loader). Converting to a single text string (D) contradicts the chunking purpose and risks truncation. Oracle's documentation on DBMS_VECTOR_CHAIN emphasizes chunking for optimizing vector quality in RAG.


NEW QUESTION # 29
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 helps differentiate between chunks from different files but has no impact on vectorization
  • 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 preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file

Answer: D

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 # 30
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

  • A. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
  • B. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
  • C. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
  • D. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output

Answer: D

Explanation:
Building a RAG application in Oracle 23ai using PL/SQL follows a logical sequence: (1) Load Document (e.g., via SQL*Loader) into the database; (2) Split Text into Chunks (e.g., DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) to manage token limits; (3) Load ONNX Model (e.g., via DBMS_VECTOR) for embedding generation; (4) Create Embeddings (e.g., UTL_TO_EMBEDDINGS) for the chunks; (5) Vectorize Question (using the same model) when a query is received; (6) Perform Vector Search (e.g., VECTOR_DISTANCE) to find relevant chunks; (7) Generate Output (e.g., via DBMS_AI with an LLM). Option B matches this flow. A starts with the model prematurely. C prioritizes the question incorrectly. D is close but loads the model too early. Oracle's RAG workflow documentation outlines this document-first approach.


NEW QUESTION # 31
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