Oracle Autonomous Database Workshop 2024 ELS — Lesson 3: APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration

Learning Objectives

• Assess the dependencies, controls, and expected business outcome for APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration.
• Demonstrate the workflow and leave reproducible evidence in application prototype and workload performance record.
• Recommend a preventive improvement after resolving an automatic index improving one query while harming another workload.

Detailed Topics

• APEX
• Automatic indexing
• Machine learning
• Monitoring
• Scaling
• And performance exploration
• Decision ownership, prerequisites, and acceptance evidence for APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration
• Relevant access, data, integration, performance, recovery, and support considerations

Instructor Demonstration

The instructor builds a representative scenario for APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration, narrating each decision and showing how it contributes to a hands-on Autonomous Database implementation covering provisioning, data access, development tools, security, monitoring, and automation. The demonstration records application prototype and workload performance record so learners can compare configuration intent with observable behaviour.

Hands-On Exercise

Complete a guided task covering APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration. After establishing a working baseline, reproduce “an automatic index improving one query while harming another workload,” isolate the responsible setting or data condition, apply a reversible fix, and update application prototype and workload performance record with before-and-after proof.

1. Map the current process and mark where the expected result can first be observed.
2. Run the scenario while preserving timestamps, job identifiers, and relevant output.
3. Apply a reversible correction only after evidence supports the cause.
4. Retest the original case plus an unaffected case and update the handover record.

Scenario Analysis

• Clarify the business trigger, affected roles, required records, and acceptable result for APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how application prototype and workload performance record should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

For a failure in APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration, separate access, setup, data, integration, and timing causes. Compare the observed sequence with application prototype and workload performance record; if “an automatic index improving one query while harming another workload” is confirmed, make the smallest reversible correction and document why broader changes were rejected.

Validation Checklist

• Prerequisites and role permissions for APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration have been confirmed before execution.
• The result agrees with the expected values documented in application prototype and workload performance record.
• Exceptions are explained with evidence rather than hidden by manual correction.
• The final record names the approver, monitoring point, and next improvement action.

Lesson Outcome

Learners can apply APEX, automatic indexing, machine learning, monitoring, scaling, and performance exploration, explain the decisions made, and defend the result with application prototype and workload performance record.