An enterprise AI governance team wants to implement a model versioning and approval process to ensure that only vetted AI models are deployed to production. Before a model is promoted, it must pass security scans, bias evaluations, and compliance checks. Which Azure service best supports implementing this approval gate in an MLOps pipeline?

A is correct because Azure DevOps (or GitHub Actions with environment protection rules) supports MLOps pipelines with deployment gates. These gates can include automated checks (security scans, bias evaluation scripts, compliance checks) and manual approval requirements (from security and governance reviewers) before a model is promoted to a production environment. This governance workflow ensures models are vetted before deployment. B is wrong because Azure Machine Learning compute clusters handle the computational resources for training and inference; they are not governance workflow tools. C is wrong because while Microsoft Purview can catalog and classify models, it does not provide a pipeline approval workflow for model promotion gates. D is wrong because Azure Policy can enforce tagging of deployed ML models but cannot implement a sequential approval workflow for model promotion.