AD141 Python Programming with Red Hat — Lesson 4: Automation Scripts, APIs, Testing, Code Quality, and Operational Delivery

Learning Objectives

• Explain the structures, services, data, settings, roles, and dependencies governing automation scripts and API interaction.
• Implement and validate testing, code quality, and delivery and documentation using reviewable evidence.
• Evaluate the condition where a technically valid implementation creates operational risk because ownership, monitoring, rollback, or handover controls are incomplete and select a proportionate corrective response.

Detailed Topics

• automation scripts applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• API interaction applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• testing applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• code quality applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• delivery and documentation applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.

Instructor Demonstration

The instructor traces a systems professional building maintainable Python utilities for data handling and operational automation. The walkthrough connects navigation, commands, configuration logic, system messages, and evidence captured in the Python utility repository, test evidence, code-review checklist, and automation implementation guide. It also tests the exception where a technically valid implementation creates operational risk because ownership, monitoring, rollback, or handover controls are incomplete.

Hands-On Exercise

Complete a controlled Red Hat exercise covering Automation Scripts, APIs, Testing, Code Quality, and Operational Delivery and preserve the verified result in the Python utility repository, test evidence, code-review checklist, and automation implementation guide.

1. Confirm scope, prerequisites, versions, identities, dependencies, and acceptance criteria.
2. Execute the workflow while recording commands, settings, outputs, events, and system messages.
3. Test normal behavior, one boundary condition, and the stated exception.
4. Document corrections, evidence, ownership, retest criteria, rollback, and handover actions.

Scenario Analysis

• Identify which object, service, policy, setting, or sequence most strongly influences automation scripts.
• Compare two defensible responses and explain technical, security, operational, and support trade-offs.
• Determine how the Python utility repository, test evidence, code-review checklist, and automation implementation guide should be approved, retained, retested, and updated.

Troubleshooting Guidance

Start where expected behavior first differs from the observed result. Check prerequisites, identity, configuration order, version compatibility, connectivity, status, resource limits, and evidence separately. Treat “a technically valid implementation creates operational risk because ownership, monitoring, rollback, or handover controls are incomplete” as a hypothesis until commands, logs, settings, and retest outcomes confirm the cause.

Validation Checklist

• Required platform components, identities, permissions, settings, and dependencies are present.
• The Python utility repository, test evidence, code-review checklist, and automation implementation guide connects each conclusion to objective, reviewable evidence.
• Normal processing, a boundary condition, dependency behavior, and the stated exception were tested.
• Corrections, ownership, retest criteria, rollback, documentation, and operational handover are complete.

Lesson Outcome

Learners can apply Automation Scripts, APIs, Testing, Code Quality, and Operational Delivery, explain the resulting platform behavior, and defend the outcome through the Python utility repository, test evidence, code-review checklist, and automation implementation guide.