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GCP Professional Data Engineer Practice Question

Your organization is containerizing a portfolio of Java microservices that must run on both Google Cloud and an existing Amazon EKS environment for disaster-recovery purposes. Leadership insists that the same CI/CD pipeline and deployment artifacts be reused across clouds, and the operations team wants to avoid any Google-specific objects that could complicate failover. Which approach will best satisfy these portability requirements while using Google Kubernetes Engine (GKE) in production?

  • Migrate the applications to App Engine standard on GCP and to AWS Elastic Beanstalk for failover, accepting minor code rewrites for each environment.

  • Adopt GKE Autopilot with Google Cloud Load Balancing and Multi-Cluster Ingress; export GKE-generated configs and adjust them manually before applying to EKS.

  • Use Cloud Build to produce OCI-compliant images, push them to Artifact Registry, and deploy the same Kubernetes Deployments, Services, and standard Ingress resources to both GKE and EKS, running a community NGINX Ingress controller on each cluster.

  • Deploy the containers to Cloud Run on GCP and to AWS Fargate on demand, rewriting deployment descriptors for each platform's serverless model.

GCP Professional Data Engineer
Designing data processing systems
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