WGU D522 | Task 2 | Python Network Remediation, DNS Configuration & Recovery
WGU D522
Python for IT Automation
Task 2: Python Network Remediation, DNS Configuration,
System Recovery & Validation
PERFORMANCE ASSESSMENT MODEL REPORT
Field Information
Student Name [Enter student name]
Student ID [Enter student ID]
Repository URL [Enter WGU GitLab URL]
Submission Date [Month Day, Year]
Environment [Assigned lab/environment]
Python Version [Actual version]
ACADEMIC-INTEGRITY NOTE
This is an editable study/model framework. Replace every placeholder, example result, command output,
screenshot, and finding with your own work and actual lab evidence. Verify all headings and requirements
against the current official WGU D522 Task 2 instructions before submission.
, WGU D522 | Task 2 | Python Network Remediation, DNS Configuration & Recovery
Contents
Executive Summary
Academic Integrity and Evidence Standard
1. Task Overview and Objectives
2. Scenario, Assumptions, and Scope
3. Remediation Architecture
4. Python Environment and Configuration
5. Pre-Remediation Baseline
6. Network Configuration Remediation
7. DNS Configuration Remediation
8. Linux System Recovery
9. Service Recovery and Restart
10. Validation Framework
11. Idempotency and Safe Automation
12. Error Handling and Rollback
13. Security Controls
14. Logging and Audit Trail
15. Structured Reporting
16. Testing Strategy
17. Evidence Plan
18. Repository and Git Workflow
19. Troubleshooting
20. Risk Analysis
21. Results Interpretation
22. Practice Questions with Answers and Rationales
23. Scenario-Based Practice
24. Final Checklist
Conclusion
References
Student Customization Page
, WGU D522 | Task 2 | Python Network Remediation, DNS Configuration & Recovery
Executive Summary
This editable model presents a controlled Python automation workflow for network remediation, DNS
configuration, Linux system recovery, and post-remediation validation. The central design principle is to establish
a baseline, make only approved changes, verify each change, and preserve an auditable record.
The workflow treats remediation as a state transition: identify the intended state, inspect the current state, apply
the smallest safe change, validate the result, and record whether the target reached the intended state.
Key Principle
Automation should not simply execute commands. It should verify preconditions, make controlled changes,
confirm the resulting state, and stop safely when assumptions are not satisfied.
Model Deliverables
Artifact Purpose
Python remediation script Automates approved corrective actions
Configuration file Defines intended network/DNS/system state
Baseline report Documents pre-change conditions
Validation report Documents post-change conditions
Logs Provides execution trace
Rollback procedure Provides recovery path
Screenshots/evidence Shows actual laboratory execution
Repository Provides version-controlled source
Final report Explains method, results, and limitations
, WGU D522 | Task 2 | Python Network Remediation, DNS Configuration & Recovery
Academic Integrity and Evidence Standard
Use the assigned WGU scenario and exact current rubric as the controlling requirements.
Do not submit sample outputs as if they were executed results.
Replace every bracketed placeholder with your own information.
Capture original screenshots from your own environment where evidence is required.
Record the actual commands, timestamps, outputs, and repository references.
Do not include passwords, private keys, tokens, or other secrets in the report or repository.
Evidence Rule
A strong claim should be traceable to an execution result, configuration artifact, screenshot, log, test result, or
repository commit.
Evidence Quality
Weak Evidence Stronger Evidence
I fixed the DNS issue. Before/after DNS output plus configuration diff and validation result.
The service works. Service status, functional test, and timestamped log.
The script passed. Test command, actual output, and repository version.
Network was repaired. Baseline, remediation output, and post-change connectivity test.
WGU D522
Python for IT Automation
Task 2: Python Network Remediation, DNS Configuration,
System Recovery & Validation
PERFORMANCE ASSESSMENT MODEL REPORT
Field Information
Student Name [Enter student name]
Student ID [Enter student ID]
Repository URL [Enter WGU GitLab URL]
Submission Date [Month Day, Year]
Environment [Assigned lab/environment]
Python Version [Actual version]
ACADEMIC-INTEGRITY NOTE
This is an editable study/model framework. Replace every placeholder, example result, command output,
screenshot, and finding with your own work and actual lab evidence. Verify all headings and requirements
against the current official WGU D522 Task 2 instructions before submission.
, WGU D522 | Task 2 | Python Network Remediation, DNS Configuration & Recovery
Contents
Executive Summary
Academic Integrity and Evidence Standard
1. Task Overview and Objectives
2. Scenario, Assumptions, and Scope
3. Remediation Architecture
4. Python Environment and Configuration
5. Pre-Remediation Baseline
6. Network Configuration Remediation
7. DNS Configuration Remediation
8. Linux System Recovery
9. Service Recovery and Restart
10. Validation Framework
11. Idempotency and Safe Automation
12. Error Handling and Rollback
13. Security Controls
14. Logging and Audit Trail
15. Structured Reporting
16. Testing Strategy
17. Evidence Plan
18. Repository and Git Workflow
19. Troubleshooting
20. Risk Analysis
21. Results Interpretation
22. Practice Questions with Answers and Rationales
23. Scenario-Based Practice
24. Final Checklist
Conclusion
References
Student Customization Page
, WGU D522 | Task 2 | Python Network Remediation, DNS Configuration & Recovery
Executive Summary
This editable model presents a controlled Python automation workflow for network remediation, DNS
configuration, Linux system recovery, and post-remediation validation. The central design principle is to establish
a baseline, make only approved changes, verify each change, and preserve an auditable record.
The workflow treats remediation as a state transition: identify the intended state, inspect the current state, apply
the smallest safe change, validate the result, and record whether the target reached the intended state.
Key Principle
Automation should not simply execute commands. It should verify preconditions, make controlled changes,
confirm the resulting state, and stop safely when assumptions are not satisfied.
Model Deliverables
Artifact Purpose
Python remediation script Automates approved corrective actions
Configuration file Defines intended network/DNS/system state
Baseline report Documents pre-change conditions
Validation report Documents post-change conditions
Logs Provides execution trace
Rollback procedure Provides recovery path
Screenshots/evidence Shows actual laboratory execution
Repository Provides version-controlled source
Final report Explains method, results, and limitations
, WGU D522 | Task 2 | Python Network Remediation, DNS Configuration & Recovery
Academic Integrity and Evidence Standard
Use the assigned WGU scenario and exact current rubric as the controlling requirements.
Do not submit sample outputs as if they were executed results.
Replace every bracketed placeholder with your own information.
Capture original screenshots from your own environment where evidence is required.
Record the actual commands, timestamps, outputs, and repository references.
Do not include passwords, private keys, tokens, or other secrets in the report or repository.
Evidence Rule
A strong claim should be traceable to an execution result, configuration artifact, screenshot, log, test result, or
repository commit.
Evidence Quality
Weak Evidence Stronger Evidence
I fixed the DNS issue. Before/after DNS output plus configuration diff and validation result.
The service works. Service status, functional test, and timestamped log.
The script passed. Test command, actual output, and repository version.
Network was repaired. Baseline, remediation output, and post-change connectivity test.
