A batch job must use custom memory sizing to control cost. What fits?
Why A is the answer
A custom machine type
A custom machine type lets you choose memory and CPU, which controls cost.
In detail
- A custom machine type sizes memory exactly.
- Measure real usage first.
Use case
An illustrative scenario, not a real incident.
A job needs a lot of memory but few CPUs, so a custom machine type avoids paying for extras.
See it in different ways
flowchart TD
A{"Workload"} -->|"Short event handler"| B["Cloud Run functions"]
A -->|"Stateless container"| C["Cloud Run"]
A -->|"Complex container platform"| D["GKE"]
A -->|"Full VM control"| E["Compute Engine"]mindmap
root((Compute))
Cloud Run
Stateless containers
GKE
Kubernetes control
Compute Engine
Custom machine types
Spot VMs
VMware Engine
Existing VMwareflowchart LR S0["Measure usage"] --> S1["Choose CPU and memory"] --> S2["Run"]
Key point: Custom machine types fit the workload.
Why the other options are wrong
- B. A larger predefined machine
- A larger predefined machine pays for more than needed.
- C. A Cloud Armor policy
- That protects web apps.
- D. A Shared VPC host project
- That is about networking.