Oracle AI Database: New Features for Developers Live Class

Course Description

Oracle AI Database: New Features for Developers Live Class develops practical capability in developer feature landscape, compatibility, application architecture, SQL enhancements, data types, development readiness and jSON Relational Duality, JSON collections, document-relational access, transactions, schema evolution, API usage. The learning path connects architecture, configuration, development, or functional processing to ownership, security, data quality, controls, and measurable acceptance criteria.

The later modules address aI Vector Search APIs, embeddings, similarity queries, hybrid retrieval, PL/SQL integration, application patterns and multilingual Engine, JavaScript in the database, security, performance, testing, deployment governance. Learners work with feature compatibility matrix, SQL and API test results, application benchmark output, and deployment and regression checklist to distinguish configuration defects from data, access, integration, performance, compliance, or operational failures.

By completing the course, participants can support an application team evaluating and adopting Oracle AI Database developer features with secure, testable, and supportable implementation patterns. They also create an improvement plan covering validation, exception handling, rollback, ownership, monitoring, governance, and production support.

What You Will Learn

• Explain the architecture, responsibilities, and prerequisite decisions behind Developer feature landscape, compatibility, application architecture.
• Configure SQL enhancements, data types, development readiness and confirm readiness using feature compatibility matrix.
• Execute JSON Relational Duality, JSON collections, document-relational access, transactions through a traceable sequence with clear ownership and approval boundaries.
• Identify incorrect JSON or vector query behavior by inspecting SQL and API test results at the earliest reliable checkpoint.
• Integrate AI Vector Search APIs, embeddings, similarity queries into the wider workflow and document its upstream and downstream dependencies.
• Validate hybrid retrieval, PL/SQL integration, application patterns with application benchmark output, including a boundary or failure case.
• Operate Multilingual Engine, JavaScript in the database, security, performance against defined security, reliability, recovery, and support objectives.
• Resolve performance regression after adoption through evidence-led diagnosis, a reversible correction, retesting, and preventive follow-up.

SkillDynamo Training Approach

Delivery combines course-specific demonstrations, guided hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, peer review, and a final improvement plan. Learners document assumptions, settings, data, outputs, exceptions, corrective actions, rollback needs, approvals, evidence, and operational responsibilities.