Argo Workflows (D)is the correct answer because it is a Kubernetes-native workflow engine designed to define and run multi-step workflows—often withparallelization—directly on Kubernetes. Argo Workflows models workflows as DAGs (directed acyclic graphs) or step-based sequences, where each step is typically a Pod. Because each step is expressed as Kubernetes resources (custom resources), Argo can schedule many tasks concurrently, control fan-out/fan-in patterns, and manage dependencies between steps (e.g., “run these 10 jobs in parallel, then aggregate results”).

The question calls it a “GitOps engine,” but the capability being tested is “orchestrate parallel jobs.” Argo Workflows fits because it is purpose-built for running complex job orchestration, including parallel tasks, retries, timeouts, artifacts passing, and conditional execution. In practice, many teams store workflow manifests in Git and apply GitOps practices around them, but the distinguishing feature here is the workflow orchestration engine itself.

Why the other options are not best:

Flux (C)is a GitOps controller that reconciles cluster state from Git; it doesn’t orchestrate parallel job graphs as its core function.

Flagger (B)is a progressive delivery operator (canary/blue-green) often paired with GitOps and service meshes/Ingress; it’s not a general workflow orchestrator for parallel batch jobs.

Jenkins X (A)is CI/CD-focused (pipelines), not primarily a Kubernetes-native workflow engine for parallel job DAGs in the way Argo Workflows is.

So, the Kubernetes-native tool specifically used to orchestrate parallel jobs and workflows isArgo Workflows (D).

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