A Linux administrator wants to use a public generative AI chat service to refactor an Ansible playbook that will later configure production servers containing regulated customer data. Which approach BEST follows responsible-AI and data-governance best practices in this situation?
Sanitize the prompt by replacing passwords and IP addresses with placeholders, submit the snippet to the AI, then perform a peer review and standard change-control before committing the code.
Paste the entire production playbook, including credentials, into the AI chat so it can generate a fully contextual answer, and deploy the result immediately.
Turn off the AI tool's safety filters to avoid incomplete answers and trust the model's internal tests instead of performing manual reviews.
Log in from a personal account to keep prompts unlinked to the company, copy the AI output verbatim, and push it straight to the corporate Git repository.
Replacing secrets and host details with placeholders prevents accidental disclosure of regulated data, while mandatory peer review ensures that any AI-generated code is checked for accuracy, security, and compliance before it reaches the repository. The other approaches expose credentials, bypass human quality assurance, violate corporate policy, or rely on a personal account that offers no organizational oversight, all of which conflict with recommended responsible-AI practices.
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What is an Ansible playbook, and why might it need to be refactored?
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What are placeholders in the context of sanitizing data, and how are they used?
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What does responsible AI mean, and why is a peer review crucial in this scenario?
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CompTIA Linux+ XK0-006 (V8)
Automation, Orchestration, and Scripting
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