Cloud Digital Leader How Google Cloud Resources Are Managed Practice Question
Which THREE actions can help reduce Compute Engine costs? (Choose three.)
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
✓
Purchase committed use discounts for predictable workloads.
Using preemptible VMs, rightsizing instances, and implementing committed use discounts are all effective cost reduction strategies for Compute Engine.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Purchase committed use discounts for predictable workloads.
Why this is correct
Committed use discounts (CUDs) require a 1-year or 3-year commitment to a minimum level of vCPU, memory, or GPU resources in a specific region, offering up to 70% lower cost than on-demand pricing for predictable baseline workloads. By aligning committed resources with steady-state usage, you avoid the higher on-demand rates for the bulk of your consumption, while any occasional spikes can still be covered by on-demand instances without negating the CUD benefit.
- ✓
Rightsize instances based on utilization metrics.
Why this is correct
Rightsizing involves continuously analyzing utilization metrics such as CPU, memory, and network from Cloud Monitoring to identify instances that are over-provisioned. You then resize those VMs to a smaller machine type or lower the machine family, which directly reduces per-second billing costs. This is an ongoing process because utilization patterns change over time, and automated recommendations from the Recommender service can help prioritize which instances to resize first.
- ✓
Use preemptible VMs for batch workloads.
Why this is correct
Preemptible VMs (now branded as Spot VMs) are up to 60-91% cheaper than standard instances but can be terminated at any time if capacity is reclaimed by Compute Engine. They are ideal for stateless, fault-tolerant batch workloads that can tolerate interruptions, such as data processing pipelines or rendering jobs, where workload resumption is already handled. However, they are not suitable for long-running services or workloads that require high availability.
- ✗
Increase the number of persistent disk snapshots.
Why it's wrong here
Increasing the number of persistent disk snapshots is a storage optimization decision that grows your backup footprint, not a compute cost reducer. Snapshots are incremental and billed based on the amount of data stored in Cloud Storage, so each additional snapshot adds to your storage bill. While snapshots protect data, they have no impact on the compute charges incurred by running virtual machines, making this action orthogonal to compute cost reduction.
- ✗
Enable sustained use discounts by running instances continuously.
Why it's wrong here
Sustained use discounts (SUDs) are applied automatically to instances running for more than 25% of the month, without any action needed, so there is no 'enable' step. Running instances continuously purely to maximize the SUD is counterproductive because you pay for the entire run time; the discount only applies to the per-second price after the threshold, so leaving an idle VM up increases total compute cost despite the per-hour discount. The correct cost-saving approach is to shut down unused instances, not to keep them running for a discount.
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