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Last Updated: Sep 09, 2026
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Oracle 1Z0-184-25 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle AI Vector Search Professional |
| Exam Number: | 1Z0-184-25 |
| Exam Duration: | 120 minutes |
| Related Certifications: | Oracle Cloud Infrastructure Data Science Oracle Database SQL Specialist Oracle Cloud Infrastructure AI Foundations |
| Available Languages: | English |
| Exam Format: | Multiple Response, Multiple Choice |
| Recommended Training: | Oracle University AI Vector Search Training Oracle Cloud Infrastructure AI Learning Path |
| Exam Registration: | Oracle Certification Registration |
| Sample Questions: | Oracle 1Z0-184-25 Sample Questions |
| Exam Way: | Online proctored exam via Oracle certification platform |
| Pre Condition: | Recommended: Basic understanding of Oracle Cloud Infrastructure and database concepts; familiarity with AI/ML and embeddings is strongly suggested. |
| Official Syllabus URL: | https://education.oracle.com |
Oracle 1Z0-184-25 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Oracle AI Vector Search in OCI | - Vector data storage
|
| Foundations of Vector Search | - Vector embeddings concepts
|
| Security, Performance, and Optimization | - Performance tuning
|
| RAG and Generative AI Integration | - Retrieval-Augmented Generation (RAG)
|
| Vector Search Implementation | - Index creation and management
|
Common Questions About the Oracle AI Vector Search Professional Exam
The Oracle 1Z0-184-25 exam, officially titled Oracle AI Vector Search Professional, is the required test for earning the Oracle AI Vector Search Professional certification, a credential at the Professional level. Passing it validates the skills Oracle expects from certified professionals, and it can also support progress toward related credentials such as Oracle Cloud Infrastructure AI Foundations, Oracle Cloud Infrastructure Data Science, Oracle Database SQL Specialist.
Oracle asks candidates to meet the following requirement before registering: Recommended: Basic understanding of Oracle Cloud Infrastructure and database concepts; familiarity with AI/ML and embeddings is strongly suggested.. Exam policies do change, so confirm the latest details on the official exam page at https://education.oracle.com before you book.
You can book your seat through the official registration channels below:
The 1Z0-184-25 exam is delivered in the following format: Online proctored exam via Oracle certification platform.
Oracle recommends the following official training for this exam:
A course builds the theory; practice turns it into exam-day performance. Once you finish a class, the 62 practice questions from Actualtests4sure show you how the same knowledge appears in exam-style items.
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The Oracle AI Vector Search Professional exam is organized into 5 major domains. Some of the key domains include:
- Oracle AI Vector Search in OCI
- Security, Performance, and Optimization
- RAG and Generative AI Integration
Scroll up to the Exam Topics section for the complete breakdown, and use it to plan how much study time each domain deserves.
Oracle AI Vector Search Professional Sample Questions:
What is the primary function of an embedding model in the context of vector search?
- A. To execute similarity search operations within a database
- B. To transform text or data into numerical vector representations
- C. To store vectors in a structured format for efficient retrieval
- D. To define the schema for a vector database
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When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?
- A. Rely on SQL*Loader's automatic normalization of vector data
- B. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient
- C. Use sparse format for vector data
- D. Enclose vector components in curly braces ({})
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What is the primary purpose of a similarity search in Oracle Database 23ai?
- A. Optimize relational database operations to compute distances between all data points in a database
- B. To retrieve the most semantically similar entries using distance metrics between different vectors
- C. To find exact matches in BLOB data
- D. To group vectors by their exact scores
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When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?
- A. In a CSV file
- B. In the database as BLOB (Binary Large Object) data
- C. In a dedicated vector database
- D. In a binary FVEC file with the relational data in a CSV file
Explanation: Only visible for Actualtests4sure members. You can sign-up / login (it's free).
Which Python library is used to vectorize text chunks and the user's question in the following example?
import oracledb
connection = oracledb.connect(user=un, password=pw, dsn=ds)
table_name = "Page"
with connection.cursor() as cursor:
create_table_sql = f"""
CREATE TABLE IF NOT EXISTS {table_name} (
id NUMBER PRIMARY KEY,
payload CLOB CHECK (payload IS JSON),
vector VECTOR
)"""
try:
cursor.execute(create_table_sql)
except oracledb.DatabaseError as e:
raise
connection.autocommit = True
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('all-MiniLM-L12-v2')
- A. json
- B. sentence_transformers
- C. oracledb
- D. oci
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