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Oracle AI Vector Search Certified Professional Quick Facts
The Oracle AI Vector Search Certified Professional exam empowers professionals to showcase advanced expertise in Oracle’s innovative AI vector capabilities. This overview provides you with a clear, structured guide so you can focus on building confidence in your skills and preparing effectively for success.
Why pursue the Oracle AI Vector Search Certified Professional certification?
This certification validates your hands-on knowledge of Oracle’s powerful AI vector technologies, including managing vector data, accelerating similarity search, using embeddings, and implementing retrieval-augmented generation (RAG) applications. It is designed for professionals who are building AI-powered solutions and want to demonstrate their ability to harness Oracle’s advanced database capabilities. Whether you are a database developer, AI engineer, or solutions architect, this certification equips you with the skill set to bring intelligent search and AI-driven applications to life.
Exam Domains Covered (Click to expand breakdown)
Exam Domain Breakdown
Domain 1: Understand Vector Fundamentals (20% of the exam)
Understand Vector Fundamentals
- Use Vector Data type for storing embeddings and enabling semantic queries
- Use Vector Distance Functions and Metrics for AI vector search
- Perform DML Operations on Vectors
- Perform DDL Operations on Vectors
Summary: This section focuses on the foundations of working with vectors inside the Oracle ecosystem. You will learn how to store embeddings effectively, enabling semantic queries that deliver meaningful results beyond traditional keyword search. Core topics include leveraging the vector data type to enrich database functionality and how distance functions and metrics form the heart of vector similarity operations.
Additionally, you will practice managing vectors with DML and DDL operations, ensuring you are comfortable with creating, inserting, and maintaining vector data. By mastering these skills, you will have the hands-on ability to lay a proper foundation for advanced AI search capabilities, giving you the confidence to work with the essential building blocks of vector search technology.
Domain 2: Using Vector Indexes (15% of the exam)
Using Vector Indexes
- Create Vector Indexes to speed up AI vector search
- Use HNSW Vector Index for search queries
- Use IVF Vector Index for search queries
Summary: This section highlights the importance of indexing for optimizing vector search. You will learn how indexes drastically improve search performance by reducing the search space and increasing the speed of similarity queries. Special focus is placed on practical implementation, so you can create indexes that meet the unique needs of large datasets and real-time applications.
You will also dive into specific index types, including Hierarchical Navigable Small Worlds (HNSW) and Inverted File (IVF) indexes. By exploring their strengths and use cases, you’ll develop an understanding of when to apply each approach, ensuring you can design searches that balance precision, recall, and performance for scalable AI-driven applications.
Domain 3: Performing Similarity Search (15% of the exam)
Performing Similarity Search
- Perform Exact Similarity Search
- Perform approximate similarity search using Vector Indexes
- Perform Multi-Vector similarity search for multi-document search
Summary: This section introduces you to the core functionality of semantic vector search: finding similarity. You will learn how to perform both exact and approximate similarity searches, gaining insight into how each approach serves different application needs. Exact search provides precision, while approximate search is vital for high-performance applications with massive datasets.
Multi-vector search is also covered in depth, teaching you how to handle scenarios that involve multi-document search, such as retrieving complex results from multiple embeddings. By mastering similarity search techniques, you will be able to design intelligent applications that quickly surface insights, delivering richer and more accurate user experiences.
Domain 4: Using Vector Embeddings (15% of the exam)
Using Vector Embeddings
- Generate Vector Embeddings outside the Oracle database
- Generate Vector Embeddings inside the Oracle database
- Store Vector Embeddings in Oracle database
Summary: This section is all about working with vector embeddings, which represent the essence of unstructured data such as text, images, or audio. You will practice generating embeddings both within Oracle Database and with external tools or frameworks, gaining flexibility in how you create and manage these rich data representations.
Once embeddings are generated, you’ll also learn how to store them efficiently within Oracle. This ensures embeddings are ready to be queried and indexed for AI-powered operations, completing the pipeline from raw data to advanced semantic search capabilities. The knowledge here is crucial to building practical systems that bridge unstructured data with structured AI-driven workflows.
Domain 5: Building a RAG Application (25% of the exam)
Building a RAG Application
- Understand Retrieval-augmented generation (RAG) concepts
- Create a RAG application using PL/SQL
- Create a RAG application using Python
Summary: This section takes your skills to the next level by focusing on RAG (retrieval-augmented generation), a strategy that enhances generative AI models with data retrieved from Oracle Database. You will explore the core principles of RAG, understanding how it improves accuracy and reduces hallucination in generative outputs by grounding responses in authoritative data.
Hands-on practice involves building your own applications with both PL/SQL and Python. This dual approach lets you integrate RAG workflows directly into Oracle environments or extend them using popular programming languages. By mastering RAG, you’ll be well-prepared to deliver AI applications that combine the strengths of Oracle search with intelligent generative models.
Domain 6: Leveraging related AI capabilities (10% of the exam)
Leveraging related AI capabilities
- Use Exadata AI Storage to accelerate AI vector search
- Use Select AI with Autonomous to query data using natural language prompts
- Use SQL Loader for loading vector data
- Use Oracle Data Pump for loading and unloading vector data
Summary: This section helps you expand your toolkit by working with Oracle’s advanced AI-related capabilities. From speeding up searches with Exadata AI Storage to using Select AI for natural language querying, you will gain insights into how Oracle products are designed to make AI functionality seamless and efficient.
In addition, you’ll explore essential tools for managing and moving vector data, such as SQL Loader and Oracle Data Pump. With these, you can accelerate workflows, support large-scale data operations, and unlock greater efficiency across your AI-powered applications. This holistic view ensures you can integrate vector search not just in theory, but as a robust and scalable part of enterprise systems.
Who should consider earning the Oracle AI Vector Search Certified Professional certification?
The Oracle AI Vector Search Certified Professional exam is designed for professionals who want to bridge the world of traditional databases with emerging artificial intelligence applications. It is an ideal certification for:
- Database Administrators (DBAs) looking to expand their expertise into AI-driven applications
- AI and Machine Learning Engineers who want to leverage Oracle Database 23ai for high-performance vector operations
- Cloud Developers aiming to integrate semantic search and Retrieval-Augmented Generation (RAG) capabilities into enterprise systems
- Data and AI enthusiasts eager to explore how Oracle is driving the next generation of AI-powered databases
If you are interested in blending data management with cutting-edge AI technologies, this certification is an excellent opportunity to stand out in a rapidly growing field.
What jobs can someone get after passing the Oracle AI Vector Search Professional exam?
This certification opens up opportunities across data, AI, and cloud engineering roles. By demonstrating knowledge of Oracle Database 23ai’s AI features, you position yourself for roles such as:
- Oracle Database Administrator (with AI specialization)
- AI/ML Engineer working with structured and vector data
- Cloud Application Developer integrating AI services
- Data Engineer building scalable, AI-powered pipelines
- Solutions Architect designing RAG-enabled, AI-driven enterprise apps
With AI-driven search becoming a key enabler in knowledge management and enterprise databases, certified professionals are in high demand for AI transformation initiatives.
What is the official exam code for Oracle AI Vector Search Professional?
The official certification exam code is 1Z0-184-25. This code uniquely identifies the Oracle AI Vector Search Certified Professional exam and is used during both registration and scheduling. Remember this exam code when purchasing the exam or seeking study materials.
How much time do you get to complete the Oracle AI Vector Search certification exam?
You will have 90 minutes to complete the exam. The time is carefully allocated to allow you to read through questions, interpret use cases, and select the best answer. Since the exam is multiple-choice, candidates should pace themselves by spending no more than 1 to 2 minutes per question. Effective time management ensures you leave space to review any flagged questions before submission.
How much does the Oracle AI Vector Search exam cost?
The exam price is $245 USD. Prices may vary slightly by location depending on taxes or regional fees. This investment demonstrates a high-value credential that helps position you at the forefront of AI database applications, especially as organizations increasingly seek experts who can blend data and AI. Companies value this certification because it shows a commitment to innovation and enterprise-level AI use cases.
How many questions are included in the Oracle 1Z0-184-25 exam?
There are 50 multiple-choice questions on the exam. Each question requires you to select the most accurate answer, and some may focus on conceptual understanding while others test practical knowledge of Oracle Database 23ai features. With a manageable number of questions spread across 90 minutes, candidates can focus on accuracy and thoughtful responses.
What score do you need to pass the Oracle AI Vector Search Professional certification?
You must earn a minimum score of 68% to pass the exam. This means that out of 50 questions, you’ll need to get at least 34 correct. Oracle uses a percentage-based evaluation, so every correct answer counts toward your overall passing grade. Unlike some certifications, you don’t need to separately pass each individual section—your cumulative performance is what matters.
What exam format should I expect on the Oracle AI Vector Search 1Z0-184-25 test?
The exam is presented in a multiple-choice format. Each question offers several options, but only one best answer should be selected. While the format is straightforward, the questions are designed to test both conceptual knowledge and practical applications such as vector indexes, embeddings, and similarity search in Oracle Database 23ai. This ensures you not only memorize terms but also understand how to apply them in real-life scenarios.
What languages are available for the Oracle AI Vector Search certification exam?
Currently, the exam is available in English. While Oracle continually expands language coverage for global candidates, English is the supported language for this certification at present. Candidates worldwide can take part by preparing with English study guides, practice exams, and Oracle training resources.
What is the main focus of the Oracle AI Vector Search Professional certification?
This exam validates your expertise in integrating artificial intelligence with database management using Oracle Database 23ai. You will be tested on concepts such as:
- Storing vectors and embeddings within Oracle Database
- Applying vector indexes like HNSW and IVF
- Executing semantic and similarity searches
- Building Retrieval-Augmented Generation (RAG) applications with PL/SQL and Python
- Leveraging Exadata AI storage and Select AI
This certification empowers professionals to unlock new opportunities for data retrieval, AI-powered search, and enterprise optimization.
What domains are covered in the Oracle AI Vector Search exam, and what are their weightings?
The exam is divided into six core domains, each with a percentage weight reflecting its importance:
-
Understand Vector Fundamentals (20%)
- Vector datatype usage for semantic queries
- Vector metrics and distance functions
- DML and DDL operations with vectors
-
Using Vector Indexes (15%)
- Vector index creation
- HNSW index usage
- IVF index usage
-
Performing Similarity Search (15%)
- Exact similarity search
- Approximate similarity search
- Multi-vector similarity search
-
Using Vector Embeddings (15%)
- Generate embeddings inside Oracle
- Generate embeddings outside Oracle
- Store vector embeddings in Oracle
-
Building a RAG Application (25%)
- RAG concepts
- RAG with PL/SQL
- RAG with Python
-
Leveraging Related AI Capabilities (10%)
- Exadata AI storage
- Oracle Select AI for natural language prompts
- SQL Loader and Data Pump for vector data
Understanding these weightings helps you prioritize study time and ensure balanced preparation.
Is prior experience required before attempting the Oracle AI Vector Search exam?
There are no formal prerequisites, making this certification accessible. However, Oracle recommends having:
- A basic understanding of Python programming
- Familiarity with AI and machine learning concepts
- Some hands-on experience with databases
This foundational background ensures you can quickly grasp the concepts tested on the exam.
How can I best prepare for the Oracle AI Vector Search 1Z0-184-25 exam?
A combination of Oracle University training, documentation review, and practical hands-on exercises offers the best preparation. Oracle also provides optional learning subscriptions with labs to deepen your understanding. To truly boost your readiness, practicing with high-quality Oracle AI Vector Search practice exams is essential for simulating the test environment and enhancing confidence with real exam-style questions.
How long is the Oracle AI Vector Search Professional certification valid?
The certification validates your expertise for the Oracle Database 23ai release. Like other Oracle certifications, it remains tied to that product version. As Oracle evolves its AI features, new certifications and updates may be released. To maintain relevance, professionals can upskill with future Oracle certifications as the platform grows.
What kinds of skills will I gain by studying for this exam?
You will develop advanced insights into:
- Handling vector data storage and indexing
- Running semantic and similarity-based searches
- Embedding and managing large-scale AI-generated vectors inside Oracle
- Building real-world RAG-powered applications with Python and PL/SQL
- Leveraging Oracle’s infrastructure tools like Select AI and Exadata AI storage
These skills are directly applicable in enterprise AI and database management, giving you immediate, practical value in your career.
Can I take the Oracle AI Vector Search exam online?
Yes. Oracle partners with Pearson VUE, allowing you to take the exam either online with a remote proctor or at an authorized testing center. Online testing offers flexibility, while in-person exams provide a controlled testing environment. Both formats maintain strict security and integrity to ensure fairness.
How do I register for the Oracle AI Vector Search certification exam?
To register, visit the official Oracle AI Vector Search Professional certification page. From there, you can choose whether to take the test in person at a Pearson VUE testing center or remotely online. You’ll be able to select your date, pay for the exam, and receive confirmation with details about your test session.
How will I get my Oracle AI Vector Search exam results?
Your score will be displayed immediately on your screen after completing the exam. Within a short time, you will also receive an email notifying you of your result. Full score reports and performance breakdowns are available via Oracle CertView, giving you valuable insights into your strengths and areas to improve.
What certification do I earn after passing the Oracle AI Vector Search Professional exam?
Once you pass, you will earn the prestigious title of Oracle AI Vector Search Certified Professional. This credential signals to employers and peers that you have cutting-edge expertise in blending AI and advanced database technologies. Adding this to your professional profile demonstrates your ability to implement real-world AI search applications at scale.
What is the benefit of combining Oracle AI Vector Search with RAG applications?
Retrieval-Augmented Generation (RAG) is a leading AI technique that enhances large language model performance by retrieving relevant, context-rich data before generating responses. By mastering RAG within Oracle Database 23ai, you can build more precise, context-driven, and enterprise-ready AI applications. This makes you highly valuable to organizations that want to maximize the reliability and accuracy of AI-powered business solutions.
What should I do after earning the Oracle AI Vector Search certification?
After achieving this certification, you can continue advancing in Oracle’s AI and database certification tracks. Many professionals choose to specialize further in:
- Oracle Cloud Infrastructure certifications
- Oracle Database administration credentials
- Advanced AI or ML training programs
These certifications can complement your vector and AI expertise to expand your career opportunities.
The Oracle AI Vector Search Certified Professional certification is a powerful step forward for anyone interested in the future of databases combined with artificial intelligence. By preparing effectively, taking advantage of Oracle’s learning resources, and practicing with the right study tools, you can showcase your expertise and open doors to AI-focused careers in enterprise environments.
