A financial media company is migrating two new application components to Google Cloud. Component 1 is a stateless containerized REST API microservice that processes incoming webhooks with highly variable traffic patterns, including extended periods of zero traffic. Component 2 is a containerized nightly data reconciliation task that runs once per day on a schedule and takes approximately 90 minutes to execute. The cloud architecture team requires a design that minimizes operational infrastructure management overhead and avoids paying for idle compute resources. Which TWO compute platform options should you select? (Select TWO.)
- Deploy Component 1 to Cloud Run services with autoscaling configured to scale down to zero instances when idle.Answer
- Deploy Component 2 as a Cloud Run job triggered on a daily schedule via Cloud Scheduler.Answer
- CDeploy Component 1 on a dedicated Google Kubernetes Engine (GKE) Standard cluster to guarantee continuous pod readiness.
- DDeploy Component 2 on a single persistent Compute Engine VM instance that continuously executes a polling loop.
Answer
Deploy Component 1 to Cloud Run services (scaling to zero instances) and deploy Component 2 as a Cloud Run job triggered via Cloud Scheduler.
Cloud Run services provide fully managed hosting for stateless containerized APIs with scale-to-zero capability during zero-traffic windows. Cloud Run jobs efficiently handle containerized tasks that run to completion (up to 24 hours), avoiding idle infrastructure expenses when paired with Cloud Scheduler for invocation.
Step-by-Step Solution
Key Concept
Selecting serverless compute platforms (Cloud Run services and Cloud Run jobs) to minimize operational overhead and baseline costs for stateless HTTP workloads and containerized batch tasks.