Hello world! - مساقات جامعة النور

Jon Green Jon Green

0 دورة ملتحَق بها • 0 اكتملت الدورة

سيرة شخصية

Pass Guaranteed 2025 Valid Oracle 1Z0-184-25 Valid Test Guide

2025 Latest Itcerttest 1Z0-184-25 PDF Dumps and 1Z0-184-25 Exam Engine Free Share: https://drive.google.com/open?id=1Xf1za8-Y2FbffB1j32O-hcyYjaRa4bE3

If you want to use our 1Z0-184-25 simulating exam on your phone at any time, then APP version is your best choice as long as you have browsers on your phone. Of course, some candidates hope that they can experience the feeling of exam when they use the 1Z0-184-25 learning engine every day. Then our PC version of our 1Z0-184-25 Exam Questions can fully meet their needs only if their computers are equipped with windows system. As we face with phones and computers everyday, these two versions are really good.

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 3

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 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.

>> 1Z0-184-25 Valid Test Guide <<

Here's the Proven and Quick Way to Pass Oracle 1Z0-184-25 Exam

Our 1Z0-184-25 preparation questions deserve you to have a try. As long as you free download the demos on our website, then you will love our 1Z0-184-25 praparation braindumps for its high quality and efficiency. All you have learned on our 1Z0-184-25 Study Materials will play an important role in your practice. We really want to help you solve all your troubles about learning the 1Z0-184-25 exam. Please give us a chance to prove.

Oracle AI Vector Search Professional Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 serialize vectors into a string
C. To calculate vector distances
D. To calculate vector dimensions

Answer: A

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 # 26
When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

A. In a binary FVEC file with the relational data in a CSV file
B. In a dedicated vector database
C. In a CSV file
D. In the database as BLOB (Binary Large Object) data

Answer: B

Explanation:
When vector embeddings are generated outside the database, the storage choice must balance efficiency, scalability, and usability for similarity search. A CSV file (A) is simple and human-readable but inefficient for large-scale vector operations due to text parsing overhead and lack of indexing support. A binary FVEC file (B) offers a compact format for vectors, reducing storage size and improving read performance, but separating relational data into a CSV complicates integration and querying, making it suboptimal for unified workflows. Storing embeddings as BLOBs in a relational database (C) integrates well with structured data and supports SQL access, but it lacks the specialized indexing (e.g., HNSW, IVF) and query optimizations that dedicated vector databases provide. A dedicated vector database (D), such as Milvus or Pinecone (or Oracle 23ai's vector capabilities if internal), is purpose-built for high-dimensional vectors, offering efficient storage, advanced indexing, and fast approximate nearest neighbor (ANN) searches. For external generation scenarios, where embeddings are not immediately integrated into Oracle 23ai, a dedicated vector database is the most suitable due to its performance and scalability advantages. Oracle's AI Vector Search documentation indirectly supports this by emphasizing optimized vector storage for search efficiency, though it focuses on in-database solutions.

NEW QUESTION # 27
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

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

Answer: A

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 # 28
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

A. It is the default distance metric for Oracle AI Vector Search
B. It guarantees higher accuracy than Euclidean Distance
C. It is simpler and faster because it avoids square-root calculations
D. It supports hierarchical partitioning of vectors

Answer: C

Explanation:
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster-crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.

NEW QUESTION # 29
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A. 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
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 primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
D. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

Answer: D

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 # 30
......

From Itcerttest website you can free download part of Itcerttest's latest Oracle certification 1Z0-184-25 exam practice questions and answers as a free try, and it will not let you down. Itcerttest latest Oracle certification 1Z0-184-25 exam practice questions and answers and real exam questions is very close. You may have also seen on other sites related training materials, but will find their Source Itcerttest of you carefully compare. The Itcerttest provide more comprehensive information, including the current exam questions, with their wealth of experience and knowledge by Itcerttest team of experts to come up against Oracle Certification 1Z0-184-25 Exam.

1Z0-184-25 Latest Exam Review: https://www.itcerttest.com/1Z0-184-25_braindumps.html

P.S. Free 2025 Oracle 1Z0-184-25 dumps are available on Google Drive shared by Itcerttest: https://drive.google.com/open?id=1Xf1za8-Y2FbffB1j32O-hcyYjaRa4bE3