PMLE Scaling Prototypes into ML Models Practice Question
You want to reduce training costs by using preemptible VMs on Vertex AI for a fault-tolerant distributed training job that uses checkpointing. Which machine type should you choose in the worker pool configuration?
⚠ Common exam trap
The trap is using the legacy GCE term 'preemptible' or assuming auto-restart alone provides cost savings, when Vertex AI requires the explicit 'spot: true' field in the machine spec and checkpointing for fault tolerance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use spot VMs by setting 'spot' to true in the machine spec
Vertex AI supports spot VMs (the successor to preemptible VMs) by setting the 'spot' field to true in the machine spec of the worker pool. Spot VMs offer up to 60-91% discounts and are suitable for fault-tolerant jobs with checkpointing, since they can be preempted with 30 seconds' notice. This is the documented, supported configuration for cost-optimized training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use spot VMs by setting 'spot' to true in the machine spec
Why this is correct
Setting spot to true in the machine spec provisions spot (preemptible) VMs, which cost substantially less than standard VMs. Checkpointing makes the fault-tolerant job resilient to preemption, satisfying the requirement to reduce training costs while tolerating interruptions.
- ✗
Use custom machine types with preemptible flag
Why it's wrong here
Custom machine types let you pick vCPU and memory ratios, but the preemptible setting is a provisioning flag independent of machine type selection; the stem asks which machine type to choose, and custom shapes do not by themselves deliver the cost reduction. Custom types are tempting when a workload needs a non-standard vCPU-to-memory ratio unavailable in predefined families.
- ✗
Use standard VMs and rely on Vertex AI auto-restart
Why it's wrong here
Standard VMs are billed at on-demand rates and auto-restart does not change that; the stem explicitly requires preemptible VMs to cut costs, so relying on auto-restart leaves the pricing model unchanged. Auto-restart is tempting because it addresses fault tolerance, and would suit a job needing resilience without cost constraints.
- ✗
Use TPU VMs because they are cheaper
Why it's wrong here
TPUs are not typically cheaper and are not spot instances.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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