EXAM 1Z0-184-25 ASSESSMENT & LATEST RELIABLE 1Z0-184-25 TEST PREP ENSURE YOU "PASS GUARANTEED"

Exam 1Z0-184-25 Assessment & Latest Reliable 1Z0-184-25 Test Prep Ensure you "Pass Guaranteed"

Exam 1Z0-184-25 Assessment & Latest Reliable 1Z0-184-25 Test Prep Ensure you "Pass Guaranteed"

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Tags: Exam 1Z0-184-25 Assessment, Reliable 1Z0-184-25 Test Prep, Composite Test 1Z0-184-25 Price, New 1Z0-184-25 Test Bootcamp, 1Z0-184-25 Valid Exam Format

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.
Topic 3
  • Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
  • SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.
Topic 4
  • 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 5
  • Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.

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1Z0-184-25 Actual Real Questions: Oracle AI Vector Search Professional & 1Z0-184-25 Practice Questions

Every Oracle aspirant wants to pass the Oracle 1Z0-184-25 exam to achieve high-paying jobs and promotions. The biggest issue 1Z0-184-25 exam applicants face is that they don't find credible platforms to copyright 1Z0-184-25 exam dumps. When candidates don't locate actual Oracle AI Vector Search Professional (1Z0-184-25) exam questions they prepare from outdated material and ultimately lose resources. If you are also facing the same problem then you are at the trusted spot.

Oracle AI Vector Search Professional Sample Questions (Q33-Q38):

NEW QUESTION # 33
A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?

  • A. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
  • B. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
  • C. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
  • D. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH

Answer: D

Explanation:
VECTOR_MEMORY_SIZE in Oracle 23ai controls memory allocation for vector operations (e.g., indexing, search) in the SGA. For a PDB, ALTER SYSTEM adjusts parameters, andSCOPE=BOTH (A) applies the change immediately and persists it across restarts (modifying the SPFILE). Syntax: ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH sets it to 1 GB. Option B (ALTER DATABASE) is invalid for this parameter, and SCOPE=VECTOR isn't a valid scope. Option C (SCOPE=SGA) isn't a scope value; valid scopes are MEMORY, SPFILE, or BOTH. Option D (RESET) reverts to default, not sets a value. In a PDB, this must be executed in the PDB context, not CDB, and BOTH ensures durability-key for production environments where vector workloads demand consistent memory.


NEW QUESTION # 34
In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?

  • A. VARCHAR2
  • B. VECTOR
  • C. VECTOR2
  • D. BLOB

Answer: B

Explanation:
Oracle Database 23ai introduces the VECTOR data type (C) specifically for storing vector embeddings used in similarity search, supporting dimensions and formats (e.g., FLOAT32, INT8). VECTOR2 (A) doesn't exist. BLOB (B) can store binary data, including vectors, but lacks the semantic structure and indexing support of VECTOR. VARCHAR2 (D) is for text, not numerical arrays. VECTOR is optimized for AI vector search with native indexing (e.g., HNSW, IVF), as per Oracle's documentation.


NEW QUESTION # 35
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

  • A. Apply relational filters and a similarity search in the query
  • B. Perform the similarity search without a WHERE clause
  • C. Use VECTOR_INDEX_HINT and NO WHERE clause
  • D. Use UNION ALL with vector operations

Answer: A

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.


NEW QUESTION # 36
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

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

Answer: A


NEW QUESTION # 37
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

  • A. It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
  • B. It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights
  • C. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
  • D. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers

Answer: C

Explanation:
RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts. This (A) improves context-awareness and precision, leveraging enterprise-specific data without retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training specialized LLMs (C) is not RAG's purpose; it uses existing models. Real-time streaming (D) is possible but not the primary benefit, as RAG focuses on stored data retrieval. Oracle's RAG documentation emphasizes private data integration for better LLM outputs.


NEW QUESTION # 38
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