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.

"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."

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