1.3Standard
Continuously update model code and data definitions.
Deploy only changes that meet quality conditions.
Choose two pipeline practices.
× Incorrect
Track code, data, model versions, and evaluation results, then require quality, safety, bias, and approval gates before staged deployment.
Detailed explanation
CorrectRecord code, data, model versions, and evaluation results as artifacts
Changes are reproducible and traceable.
Changes are reproducible and traceable.
CorrectRequire fixed tests, bias and safety checks, and approval before staged rollout
Regressions and risk are gated.
Regressions and risk are gated.
IncorrectDeploy directly whenever data changes
No quality gate exists.
No quality gate exists.
IncorrectDelete failed evaluation results
Auditability is lost.
Auditability is lost.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3のMLOps、自動化、承認を確認する。Expected result
モデル変更へ再現性と品質ゲートを組み込める。Key points
- CI/CD
- Quality gate
- Staged rollout
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.
Question IDAWS-AIF-318
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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