While using a large language model (LLM) assistant to generate Terraform/OpenTofu code for provisioning cloud infrastructure, which additional control should you implement before merging the assistant's output into the GitOps repository to most effectively follow CompTIA Linux+ AI best-practice guidance for generating infrastructure as code?
Archive the AI-generated Terraform in an encrypted ZIP file and apply it manually in production to protect secrets from the pipeline.
Raise the model's temperature and prompt it to self-verify, then push the resulting code directly to the main branch.
Add a policy-as-code scanner such as Checkov to the CI pipeline so the build fails if the generated Terraform violates security or compliance rules.
Merge the assistant's code to main immediately and depend on runtime monitoring tools to roll back any misconfigurations.
CompTIA Linux+ best practices for AI-generated content emphasize verifying the assistant's output and integrating quality-assurance measures before code is accepted. Running an automated policy-as-code scan in the continuous-integration pipeline provides an objective, repeatable gate that blocks non-compliant resources before they ever reach the main branch. Encrypting or archiving code does nothing to validate correctness, increasing the model temperature prioritizes creativity over compliance, and relying solely on production monitoring shifts risk to runtime instead of catching issues early.
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CompTIA Linux+ XK0-006 (V8)
Automation, Orchestration, and Scripting
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