100% Pass Quiz 2025 Perfect Oracle New 1Z0-184-25 Exam Test

We provide free update to the client and the discounts to the old client. We provide free update of our 1Z0-184-25 exam materials within one year and after one year the client can enjoy the 50% discounts. The old clients enjoy some certain discounts when they buy our 1Z0-184-25 exam torrent. Our experts check whether there is the update of the test bank every day and if there is the system will send to the client automatically. We choose the most typical questions and answers which seize the focus and important information and the questions and answers are based on the real exam. So you can master the most important 1Z0-184-25 Exam Torrent in the shortest time and finally pass the exam successfully.

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
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	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 3	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 4	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 5	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.

>> New 1Z0-184-25 Exam Test <<

100% Pass 2025 Oracle Marvelous New 1Z0-184-25 Exam Test

The 1Z0-184-25 torrent prep contains the real questions and simulation questions of various qualifying examinations. It is very worthy of study efficiently. Time is constant development, and proposition experts will set questions of real 1Z0-184-25 exam continuously according to the progress of the society change tendency of proposition, and consciously highlight the hot issues and policy changes. In order to be able to better grasp the proposition thesis direction, the 1Z0-184-25 study question focus on the latest content to help you pass the 1Z0-184-25 exam.

Oracle AI Vector Search Professional Sample Questions (Q17-Q22):

NEW QUESTION # 17
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. EMBED_TEXT
D. CREATE_VECTOR_EMBEDDING

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 # 18
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?

A. L1_DISTANCE
B. COSINE_DISTANCE
C. L2_DISTANCE
D. HAMMING_DISTANCE

Answer: C

Explanation:
In Oracle Database 23ai, vector distance calculations are primarily handled by the VECTOR_DISTANCE function, which supports multiple metrics (e.g., COSINE, EUCLIDEAN) specified as parameters (e.g., VECTOR_DISTANCE(v1, v2, EUCLIDEAN)). However, the question implies distinct functions, a common convention in some databases or libraries, and Oracle's documentation aligns L2_DISTANCE (B) with the Euclidean metric. L2 (Euclidean) distance is the straight-line distance between two points in vector space, computed as √∑(xi - yi)², where xi and yi are vector components. For example, for vectors [1, 2] and [4, 6], L2 distance is √((1-4)² + (2-6)²) = √(9 + 16) = 5.
Option A, L1_DISTANCE, represents Manhattan distance (∑|xi - yi|), summing absolute differences-not Euclidean. Option C, HAMMING_DISTANCE, counts differing bits, suited for binary vectors (e.g., INT8), not continuous Euclidean spaces typically used with FLOAT32 embeddings. Option D, COSINE_DISTANCE (1 - cosine similarity), measures angular separation, distinct from Euclidean's magnitude-inclusive approach. While VECTOR_DISTANCE is the general function in 23ai, L2_DISTANCE may be an alias or a contextual shorthand in some Oracle AI examples, reflecting Euclidean's prominence in geometric similarity tasks. Misinterpreting this could lead to choosing COSINE for spatial tasks where magnitude matters, skewing results. Oracle's vector search framework supports Euclidean via VECTOR_DISTANCE, but B aligns with the question's phrasing.

NEW QUESTION # 19
Which function is used to generate vector embeddings within an Oracle database?

A. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
B. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS
C. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
D. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

Answer: B

Explanation:
In Oracle 23ai, the DBMS_VECTOR_CHAIN package provides utilities for vector workflows. UTL_TO_EMBEDDINGS (C) generates vector embeddings from text within the database, typically using an ONNX model, supporting RAG and search applications. UTL_TO_CHUNKS (A) splits text, not generates embeddings. UTL_TO_TEXT (B) converts documents to text, a preprocessing step. UTL_TO_GENERATE_TEXT (D) doesn't exist; text generation is handled by LLMs, not this package. Oracle's documentation identifies UTL_TO_EMBEDDINGS as the embedding creation function in PL/SQL workflows.

NEW QUESTION # 20
What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

A. To create vector indexes for efficient searches
B. To calculate the distance between vectors using a specified metric
C. To group vectors by their exact scores
D. To fetch rows that match exact vector embeddings

Answer: B

Explanation:
The VECTOR_DISTANCE function in Oracle 23ai (D) computes the distance between two vectors using a specified metric (e.g., COSINE, EUCLIDEAN), enabling similarity search by quantifying proximity. It doesn't fetch exact matches (A); it measures similarity. Index creation (B) is handled by CREATE INDEX, not this function. Grouping (C) requires additional SQL (e.g., GROUP BY), not VECTOR_DISTANCE's role. Oracle's SQL reference defines it as the core tool for distance calculation in vector queries.

NEW QUESTION # 21
What is the significance of using local ONNX models for embedding within the database?

A. Support for legacy SQL*Plus clients
B. Enhanced security because data remains within the database
C. Reduced embedding dimensions for faster processing
D. Improved accuracy compared to external models

Answer: B

Explanation:
Using local ONNX (Open Neural Network Exchange) models for embedding within Oracle Database 23ai means loading pre-trained models (e.g., via DBMS_VECTOR) into the database to generate vectors internally, rather than relying on external APIs or services. The primary significance is enhanced security (D): sensitive data (e.g., proprietary documents) never leaves the database, avoiding exposure to external networks or third-party providers. This aligns with enterprise needs for data privacy and compliance (e.g., GDPR), as the embedding process-say, converting "confidential report" to a vector-occurs within Oracle's secure environment, leveraging its encryption and access controls.
Option A (SQLPlus support) is irrelevant; ONNX integration is about AI functionality, not legacy client compatibility-SQLPlus can query vectors regardless. Option B (improved accuracy) is misleading; accuracy depends on the model's training, not its location-local vs. external models could be identical (e.g., same BERT variant). Option C (reduced dimensions) is a misconception; dimensionality is model-defined (e.g., 768 for BERT), not altered by locality-processing speed might improve due to reduced latency, but that's secondary. Security is the standout benefit, as Oracle's documentation emphasizes in-database processing to minimize data egress risks, a critical consideration for RAG or Select AI workflows where private data fuels LLMs. Without this, external calls could leak context, undermining trust in AI applications.

NEW QUESTION # 22
......

You must believe that you have extraordinary ability to work and have an international certificate to prove your inner strength. You will definitely be the best one among your colleagues. The help you provide with our 1Z0-184-25 Learning Materials is definitely what you really need. And if you study with our 1Z0-184-25 exam braindumps, you will know your dream clearly. Join 1Z0-184-25 study guide and you will be the best person!

1Z0-184-25 Reliable Test Question: https://www.testkingpdf.com/1Z0-184-25-testking-pdf-torrent.html