Compute Practice Questions
Virtual machines, serverless runtimes and instance groups. 9 original questions with full explanations.
Question 1 of 9
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Computemedium
A small team runs a public REST API with highly variable traffic — busy during business hours and almost no requests overnight. They want to minimise both cost and operational work. Which compute option best fits?
Select an answer to see the explanation.
These questions are written by GCP Prep to test understanding of publicly documented concepts. They are not real exam questions.
What this test covers
One concept per question. If you can explain each of these without hesitating, you are in good shape on Compute.
- Scale to zero suits spiky traffic with a small operations team
- Autohealing recreates unhealthy instances; load balancer health checks only redirect traffic
- Kubernetes provides a consistent deployment target across environments
- Spot capacity suits fault-tolerant, interruptible work
- Baking a custom image minimises startup time and makes instances reproducible
- Stopping an instance halts compute charges but not all associated resource charges
- Cloud Run concurrency directly determines how many instances a given request rate needs
- Sole tenancy provides dedicated physical hardware for licensing and compliance
- Local SSD trades durability for maximum performance
Other topics
Related Certifications
- Associate Cloud EngineerThe hands-on baseline. The most widely useful place to start.
- Professional Cloud DeveloperFor engineers who build and ship cloud-native applications.
- Professional Cloud ArchitectThe flagship design exam. Long scenarios, real trade-offs.
- Professional Cloud DevOps EngineerCI/CD, SRE practice and running services that stay up.
Related Cloud Topics
- Compute EngineVirtual machines with full control over the operating system: machine types, images, disks, instance groups and autoscaling.
- Cloud RunRun a container without managing servers: request-driven scaling, scale to zero, and per-request billing.
- Google Kubernetes Engine (GKE)Managed Kubernetes: pods, deployments, services, autoscaling and the operational model that comes with a cluster.