AD141 Python Programming with Red Hat — Lesson 2: Control Flow, Functions, Modules, Packages, Comprehensions, and Reuse

Learning Objectives

• Explain the structures, services, data, settings, roles, and dependencies governing conditionals and loops.
• Implement and validate functions, modules and packages, and comprehensions using reviewable evidence.
• Evaluate the condition where a change succeeds in one environment but fails elsewhere because inventory, policy, version, or runtime assumptions differ and select a proportionate corrective response.

Detailed Topics

• conditionals applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• loops applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• functions applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• modules and packages applied to the course scenario, expected platform behavior, dependencies, controls, and evidence.
• comprehensions 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 change succeeds in one environment but fails elsewhere because inventory, policy, version, or runtime assumptions differ.

Hands-On Exercise

Complete a controlled Red Hat exercise covering Control Flow, Functions, Modules, Packages, Comprehensions, and Reuse 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 conditionals.
• 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 change succeeds in one environment but fails elsewhere because inventory, policy, version, or runtime assumptions differ” 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 Control Flow, Functions, Modules, Packages, Comprehensions, and Reuse, explain the resulting platform behavior, and defend the outcome through the Python utility repository, test evidence, code-review checklist, and automation implementation guide.