1Z0-184
Coming Soon
Expected availability announced soon
This course is in active development. Preview the scope below and create a free account to be notified the moment it goes live.
DB AI Vector Search
Oracle Database AI Vector Search Specialist certification teaches professionals how to design, implement, and optimize vector indexes and retrieval pipelines, integrating RAG and enterprise AI for high‑performance search solutions.
90
Minutes
50
Questions
65
Passing Score
$245
Exam Cost
Who Should Take This
Database administrators, data engineers, and AI solution architects who have at least two years of experience with Oracle Database and are familiar with SQL and basic machine‑learning concepts should consider this credential. They seek to validate expertise in vector search design, performance tuning, and advanced retrieval features to lead enterprise AI initiatives.
What's Covered
1
Vector Search Fundamentals
2
Vector Indexes
3
RAG and Enterprise AI
4
Performance and Optimization
5
Advanced Vector Features
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
62 learning goals
1
Vector Search Fundamentals
2 topics
Vector Concepts
- Implement VECTOR data type in Oracle 23ai with dimension specification, distance metrics, and storage configuration.
- Configure vector columns in tables with appropriate dimensionality and norm specifications for embedding storage.
- Design embedding generation pipelines using DBMS_VECTOR for creating vector representations from text and structured data.
- Evaluate vector dimensionality, distance metrics (cosine, dot product, L2), and normalization choices for search accuracy.
- Configure Vector Concepts with appropriate settings and parameters for a production deployment scenario in Oracle Database AI Vector Search Specialist.
- Assess Vector Concepts implementations against best practices to identify gaps and recommend improvements.
- Architect Vector Concepts solutions with scalability patterns and capacity planning for long-term sustainability.
Vector Operations
- Implement vector DML operations: INSERT, UPDATE, and bulk loading of vector embeddings into Oracle tables.
- Configure vector similarity search using VECTOR_DISTANCE function and approximate nearest neighbor queries.
- Design hybrid search combining vector similarity with traditional SQL WHERE clauses for filtered vector retrieval.
- Analyze vector search results to evaluate recall, precision, and relevance for different distance metric configurations.
- Evaluate Vector Operations alternatives and tradeoffs to recommend the optimal approach for given constraints.
- Formulate Vector Operations governance frameworks with policies, standards, and compliance monitoring.
- Explain how to troubleshoot common issues with Vector Operations including error messages and diagnostic procedures.
2
Vector Indexes
2 topics
HNSW Indexes
- Design HNSW (Hierarchical Navigable Small World) index configurations for approximate nearest neighbor search optimization.
- Implement HNSW vector index creation with M, efConstruction, and efSearch parameters for tuning accuracy versus speed.
- Configure IVF (Inverted File) vector indexes as an alternative to HNSW for large-scale vector collections.
- Evaluate HNSW versus IVF index tradeoffs for different dataset sizes, dimensionality, and query patterns.
- Design enterprise-grade HNSW Indexes architectures incorporating HA, DR, and security requirements.
- Apply HNSW Indexes configuration patterns to meet specific business requirements including compliance needs.
Index Management
- Implement vector index maintenance including rebuild, reorganization, and monitoring for search quality assurance.
- Design partitioned vector index strategies for managing large vector datasets across table partitions.
- Configure vector index memory management and SGA allocation for optimal index lookup performance.
- Analyze vector index performance to identify recall degradation, index bloat, and recommend maintenance actions.
- Implement Index Management following best practices for security, performance, and reliability.
- Diagnose Index Management issues by analyzing metrics, logs, and configuration to determine root causes.
3
RAG and Enterprise AI
2 topics
RAG Architecture
- Design retrieval augmented generation pipelines using Oracle Vector Search for enterprise knowledge bases.
- Implement document chunking strategies (fixed-size, semantic, recursive) for preparing text for vector embedding.
- Configure embedding model integration (OCI GenAI, OpenAI, Cohere) with DBMS_VECTOR for automated embedding generation.
- Evaluate RAG retrieval quality using precision, recall, and relevance scoring to optimize chunking and embedding strategies.
- Analyze RAG Architecture configurations to identify security vulnerabilities, bottlenecks, and optimization opportunities.
- Recommend RAG Architecture optimization strategies balancing performance, cost, and operational complexity.
Enterprise Integration
- Architect vector search integration with Oracle APEX, PL/SQL applications, and Oracle REST Data Services (ORDS).
- Implement DBMS_VECTOR_CHAIN for end-to-end RAG pipelines combining chunking, embedding, search, and generation.
- Configure vector search security with VPD policies and DBMS_RLS for row-level access control on vector data.
- Analyze enterprise RAG deployments to identify accuracy issues, latency bottlenecks, and cost optimization opportunities.
- Architect Enterprise Integration solutions with scalability patterns and capacity planning for long-term sustainability.
- Configure Enterprise Integration with appropriate settings and parameters for a production deployment scenario in Oracle Database AI Vector Search Specialist.
4
Performance and Optimization
2 topics
Query Optimization
- Design vector query optimization strategies with pre-filtering, post-filtering, and approximate versus exact search modes.
- Implement query performance tuning with vector index hints, parallel query configuration, and result caching.
- Configure DBMS_VECTOR parallel embedding generation for batch processing of large document collections.
- Analyze vector query execution plans to identify performance bottlenecks and recommend index or query adjustments.
- Explain how to troubleshoot common issues with Query Optimization including error messages and diagnostic procedures.
- Evaluate Query Optimization alternatives and tradeoffs to recommend the optimal approach for given constraints.
Scalability
- Architect scalable vector search solutions with partitioning, sharding, and distributed query patterns on Exadata.
- Implement vector data lifecycle management with archival, re-embedding, and index refresh for evolving knowledge bases.
- Configure monitoring for vector search workloads using AWR, ASH, and custom metrics for capacity planning.
- Evaluate vector search scalability to recommend infrastructure sizing, index strategies, and caching for growth.
- Compare Scalability deployment patterns to determine the best architecture for availability and scalability needs.
- Design enterprise-grade Scalability architectures incorporating HA, DR, and security requirements.
5
Advanced Vector Features
2 topics
Multi-Vector Search
- Design multi-vector search patterns combining text embeddings, image embeddings, and structured data for multimodal retrieval.
- Implement cross-lingual vector search using multilingual embedding models for international knowledge bases.
- Configure vector search with JSON Relational Duality views for combined structured and unstructured data retrieval.
- Evaluate multi-modal vector search accuracy to optimize embedding selection and fusion strategies.
- Plan Multi-Vector Search migration and modernization strategies with phased rollout and rollback procedures.
- Implement Multi-Vector Search following best practices for security, performance, and reliability.
ONNX and Custom Models
- Implement ONNX model import for running custom embedding models directly within Oracle Database for in-database ML.
- Design in-database embedding generation pipelines avoiding data movement for security-sensitive enterprise deployments.
- Configure DBMS_VECTOR with custom ONNX models for domain-specific embedding generation and similarity computation.
- Analyze custom model performance versus pre-trained embeddings to recommend the optimal approach for domain accuracy.
- Deploy ONNX and Custom Models with integration to monitoring, logging, and alerting services for operational visibility.
- Analyze ONNX and Custom Models configurations to identify security vulnerabilities, bottlenecks, and optimization opportunities.
Scope
Included Topics
- All domains in the Oracle Database AI Vector Search Specialist certification exam guide.
- Core topics: Vector Data Type, HNSW Indexes, Similarity Search, Embeddings, RAG, Vector DML, Performance.
- Oracle services, tools, and best practices relevant to this certification.
- Scenario-based problem solving at the professional level.
Not Covered
- Topics outside the official exam guide scope.
- Programming language specifics beyond the exam requirements.
- Specific pricing values that change over time.
- Third-party products beyond basic integration awareness.
Official Exam Page
Learn more at Oracle
1Z0-184 is coming soon
Adaptive learning that maps your knowledge and closes your gaps.
Create Free Account to Be Notified