New exam 1Z0-184-25: Oracle AI Vector Search Professional

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There is a new exam available at Oracle, the Oracle AI Vector Search Professional (1Z0-184-25). And since the certification is not listed under the Cloud certifications, it wont expire. Correction, it does expire in 2 years.

"The Oracle AI Vector Search Professional Certification is designed for Oracle DBAs, AI engineers, and cloud developers to unlock the potential of Oracle Database 23ai to build AI-driven applications. The target candidate for this certification should have basic familiarity in Python and AI/ML concepts. This certification bridges the gap between traditional database management and cutting-edge AI technologies by focusing on leveraging Oracle Database 23ai capabilities for handling vector data and enabling semantic and similarity searches. Through in-depth training, candidates will master techniques like vector data storage, indexing, and generating and storing embeddings, alongside advanced applications such as building Retrieval-Augmented Generation (RAG) applications using PL/SQL and Python. With insights into Exadata AI Storage, Oracle GoldenGate, and Select AI, this certification prepares professionals to integrate and optimize AI in enterprise-level databases seamlessly."

Update: check the official blog with more details and get the exam for free in next 3 months!

Free course

There 8.5 hours long course Become an Oracle AI Vector Search Professional in available on Mylearn with following claims:

Upon completion of this Learning Path, you will be able to:

• Understand and implement vector data type within Oracle Database 23ai.
• Generate and store vector embeddings.
• Perform exact and approximate similarity searches.
• Create and optimize vector indexes such as HNSW and IVF for AI vector search.
• Develop Retrieval-Augmented Generation (RAG) applications using PL/SQL and Python.
• Understand Exadata AI Storage and Distributed AI Processing with Oracle GoldenGate.
• Load and manage vector data efficiently using SQL Loader and Oracle Data Pump.
• Query data using natural language prompts with Select AI.

Skills you will learn:

• Building Retrieval-Augmented Generation (RAG) applications with Oracle Database 23ai.
• Generating vector embeddings both inside and outside Oracle database 23ai.
• Designing and executing vector similarity searches.
• Creating and managing HNSW and IVF vector indexes for optimized search performance.
• Leveraging Exadata and GoldenGate for enhanced AI processing and vector search acceleration.
• Loading, unloading, and managing vector datasets effectively.
• Utilizing Select AI and Autonomous Database for natural language querying.

Exam topics

Understand Vector Fundamentals (20%)

• 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

Using Vector Indexes (15%)

• Create Vector Indexes to speed up AI vector search
• Use HNSW Vector Index for search queries
• Use IVF Vector Index for search queries

Performing Similarity Search (15%)

• Perform Exact Similarity Search
• Perform approximate similarity search using Vector Indexes
• Perform Multi-Vector similarity search for multi-document search

Using Vector Embeddings (15%)

• Generate Vector Embeddings outside the Oracle database
• Generate Vector Embeddings inside the Oracle database
• Store Vector Embeddings in Oracle database

Building a RAG Application (25%)

• Understand Retrieval-augmented generation (RAG) concepts
• Create a RAG application using PL/SQL
• Create a RAG application using Python

Leveraging related AI capabilities (10%)

• 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