GitOps for ML with Argo CD Quiz

Answers and explanations appear as you take the quiz.

1. What does GitOps treat as the declared desired deployment state?
   • Version-controlled configuration in a Git repository
   • The current state of a developer's terminal
   • The most recent prediction log
   • The model's training accuracy only
2. What does Argo CD do when live cluster state differs from Git?
   • Automatically retrains the model
   • Reports drift and may reconcile according to configured sync policy
   • Changes the source repository to match the cluster
   • Rewrites the model artifact
3. What does self-heal generally do in an Argo CD setup?
   • Restore deleted training data
   • Repair model accuracy after drift
   • Reconcile manual live changes back toward declared Git state
   • Rotate every cloud secret
4. Why can automated sync be risky with an unreviewed manifest change?
   • It guarantees no rollback is possible
   • It disables Kubernetes health checks
   • It changes Git history automatically
   • It can propagate an incorrect desired state quickly
5. How should a model image be referenced for traceable deployment?
   • By a mutable latest tag only
   • By an immutable digest or version tied to validation evidence
   • By the training dataset's filename
   • By an unrecorded local path
6. What does Kubernetes health status establish in this context?
   • That data are free of bias
   • That the model is calibrated
   • Resource health, not prediction quality
   • That the candidate beat its baseline
7. Why may a Git revert not fully restore an ML deployment?
   • Kubernetes ignores image versions
   • Git cannot track text manifests
   • Argo CD requires retraining to sync
   • External state such as registry aliases or data migrations may not roll back
8. Where should deployment credentials be stored?
   • In a secret-management mechanism referenced by configuration
   • As plaintext in a committed manifest
   • Inside model weights
   • In the public image tag