A systems administrator uses a large language model (LLM) to generate an Ansible playbook designed to configure a fleet of new web servers. The playbook appears correct at a glance. What is the most important best practice the administrator must follow before executing the playbook on production systems?
Add comments to the playbook crediting the AI model that generated it and noting the date of generation.
Check the playbook's syntax using an automated linter without manually reviewing the logic.
Immediately run the playbook on a single production node to confirm it works before running it on the entire fleet.
Thoroughly review the playbook for correctness and security, and then test it in a staging environment that mirrors production.
The correct answer is to thoroughly review the playbook for correctness and security, and then test it in a staging environment. AI-generated code, including Infrastructure as Code (IaC), can contain subtle errors, security vulnerabilities, or use deprecated modules. It is the administrator's responsibility to validate any code before deployment. A thorough manual review combined with testing in a safe, non-production environment that mimics the production setup is the most critical step to ensure the playbook is safe and effective. Running the playbook on a single production node is dangerous as it could still cause an outage or security issue. Checking syntax with a linter is a good step, but it is insufficient as it will not catch logical errors or security misconfigurations. Adding comments for attribution is good practice for documentation but does not address the operational risk of running unverified code.
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