Oracle AI Autonomous Database Professional

Course Description

Oracle AI Autonomous Database Professional builds practical capability across Autonomous Database architecture, workload types, provisioning, connectivity, Data loading, SQL tools, APEX. Rather than treating the application as a set of isolated screens, the course follows the decisions, dependencies, roles, and evidence needed to make the process dependable in day-to-day operations.

The learning journey then moves into Security, encryption, users, network controls, Performance, auto indexing, monitoring. Activities use Autonomous Database design and provisioning evidence, loaded dataset with clone and recovery validation, security baseline and advanced-feature demonstration, performance comparison and production-readiness checklist to compare expected behaviour with actual output, isolate defects early, and document corrective decisions without losing traceability.

By completion, participants can contribute to a secure Autonomous Database solution that uses automation, analytics, AI features, and resilient data-management practices. The final improvement plan identifies practical follow-up actions for monitoring, ownership, exception handling, support readiness, and controlled change.

What You Will Learn

• Explain how Autonomous Database architecture, workload types, and provisioning establish the operating foundation and shape later configuration decisions.
• Configure connectivity, scaling, and lifecycle choices, then confirm readiness through Autonomous Database design and provisioning evidence rather than relying on screen messages alone.
• Execute a controlled workflow covering Data loading, SQL tools, and APEX, preserving input traceability, approvals, and the resulting changes.
• Read loaded dataset with clone and recovery validation to isolate a data load silently rejecting rows because of format differences, separate symptoms from causes, and select a correction that can be safely retested.
• Integrate Security, encryption, and users with ownership, access, and downstream process expectations.
• Challenge network controls, auditing, spatial, graph, and generative AI capabilities using normal and boundary cases, then defend the outcome with security baseline and advanced-feature demonstration.
• Investigate automatic scaling increasing cost without a workload-based threshold policy by reviewing Performance, auto indexing, and monitoring before applying a reversible fix.
• Prepare performance comparison and production-readiness checklist that demonstrates cost control, high availability, and production governance and supports handover, monitoring, and the next improvement decision.

SkillDynamo Training Approach

Delivery combines focused demonstrations, guided build activities, scenario comparison, evidence review, fault isolation, peer discussion, and a final improvement plan. Each activity requires learners to record assumptions, decisions, expected results, observed behaviour, and the owner of any unresolved action.