[Q13-Q31] Free Sample Questions to Practice 1Z0-184-25 Certification Test Engine [Feb-2026]

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Free Sample Questions to Practice 1Z0-184-25 Certification Test Engine [Feb-2026]

2026 Valid 1Z0-184-25 Real Exam Questions, practice Oracle Database 23ai

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.
Topic 4	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 QUESTION 13
What is the significance of using local ONNX models for embedding within the database?

Support for legacy SQL*Plus clients

Improved accuracy compared to external models

Reduced embedding dimensions for faster processing

Enhanced security because data remains within the database

NEW QUESTION 14
What is the primary purpose of a similarity search in Oracle Database 23ai?

Optimize relational database operations to compute distances between all data points in a database

To find exact matches in BLOB data

To retrieve the most semantically similar entries using distance metrics between different vectors

To group vectors by their exact scores

NEW QUESTION 15
Which function should you use to determine the storage format of a vector?

VECTOR_DIMENSION_FORMAT

VECTOR_CHUNKS

VECTOR_NORM

VECTOR_EMBEDDING

NEW QUESTION 16
When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

The choice of programming language used to process the dataset (e.g., Python, Java)

The physical location where the vector embeddings are stored

The storage format of the new dataset (e.g., CSV, JSON)

The same vector embedding model must be used for vectorizing the data and creating a query vector

NEW QUESTION 17
What happens when you attempt to insert a vector with an incorrect number of dimensions into a VECTOR column with a defined number of dimensions?

The database truncates the vector to fit the defined dimensions

The database pads the vector with zeros to match the defined dimensions

The database ignores the defined dimensions and inserts the vector as is

The insert operation fails, and an error message is thrown

NEW QUESTION 18
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?

Use UNION ALL with vector operations

Perform the similarity search without a WHERE clause

Apply relational filters and a similarity search in the query

Use VECTOR_INDEX_HINT and NO WHERE clause

NEW QUESTION 19
What is the primary function of an embedding model in the context of vector search?

To define the schema for a vector database

To execute similarity search operations within a database

To transform text or data into numerical vector representations

To store vectors in a structured format for efficient retrieval

NEW QUESTION 20
If a query vector uses a different distance metric than the one used to create the index, whathappens?

The query fails

An exact match search is triggered

The index automatically updates

A warning is logged, but the query executes

NEW QUESTION 21
You are tasked with creating a table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions, and the dimensions should be stored as 32-bitfloating point numbers. Which SQL statement should you use?

CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))

CREATE TABLE vectors (id NUMBER, embedding VECTOR)

CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))

CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))

NEW QUESTION 22
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

Both operate identically but differ in memory usage

HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy

HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions

HNSW is partition-based, whereas IVF uses neighbor graphs for indexing

NEW QUESTION 23
What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?

Real-time vector data updates across locations

Automatic translation of vector embeddings between formats

Specialized vector embedding compression

Built-in version control for vector data

NEW QUESTION 24
You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?

1 MB

4 MB

256 KB

1 GB

NEW QUESTION 25
You need to generate a vector from the string ‘[1.2, 3.4]’ in FLOAT32 format with 2 dimensions. Which function will you use?

TO_VECTOR

VECTOR_DISTANCE

FROM_VECTOR

VECTOR_SERIALIZE

NEW QUESTION 26
What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?

It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times but potentially increasing storage requirements

It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information

It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially requiring more development effort

It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM

NEW QUESTION 27
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

It is the default distance metric for Oracle AI Vector Search

It supports hierarchical partitioning of vectors

It is simpler and faster because it avoids square-root calculations

It guarantees higher accuracy than Euclidean Distance

NEW QUESTION 28
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

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

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

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

NEW QUESTION 29
Which function is used to generate vector embeddings within an Oracle database?

DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

DBMS_VECTOR_CHAIN.UTL_TO_TEXT

DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS

DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

NEW QUESTION 30
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database

Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search

Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications

Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types

NEW QUESTION 31
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

Approximate similarity search with HNSW indexing and target accuracy of 70%

Multivector similarity search with partitioning

Exact similarity search using a full table scan

Approximate similarity search with IVF indexing and target accuracy of 70%

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[Q13-Q31] Free Sample Questions to Practice 1Z0-184-25 Certification Test Engine [Feb-2026]

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