Google ACE Practice Question: Run a batch processing workload that processes 10…

A company needs to run a batch processing workload that processes 10 TB of data nightly. The job runs for 4 hours and can tolerate interruption with checkpointing. Cost must be minimized. Which Compute Engine pricing model is most appropriate for the batch VMs?

⚠ Common exam trap

Many candidates think sustained use discounts (Option C) are automatic and sufficient for any long-running workload, but they fail to realize that a 4-hour nightly job does not accumulate enough monthly usage to trigger significant discounts, making Spot VMs the clear winner for cost minimization.

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

✓

Spot VMs (preemptible pricing)

Spot VMs (preemptible pricing) are the most cost-effective choice for batch workloads that are fault-tolerant and can handle interruptions via checkpointing. Since the job runs for only 4 hours nightly and can resume from checkpoints, Spot VMs offer up to 60-91% cost savings over on-demand pricing without requiring any commitment.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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On-demand VM pricing with committed use discounts (1-year CUD)
Why it's wrong here
Committed use discounts (CUDs) require a 1-year or 3-year commitment of a consistent baseline of vCPUs, memory, and GPUs, delivering roughly 37-57% savings for predictable, always-on workloads. But this batch job runs only a few hours each night, so a CUD would lock you into paying for resources the vast majority of the time they sit idle. The per-hour cost is lower than on-demand, but the effective cost per completed job is far higher than Spot because you are paying for 24/7 capacity to support a short, interruptible workload.
✓
Spot VMs (preemptible pricing)
Why this is correct
Spot VMs (formerly preemptible VMs) offer up to 91% discount over on-demand pricing and are ideal for fault-tolerant batch jobs that can be checkpointed and resumed after interruption. With checkpointing, the nightly job can simply restart from the last saved state whenever a Spot VM is reclaimed, so transient failures do not jeopardize completion. This provides the maximum cost reduction among the options for a workload that explicitly tolerates interruption, making it the correct choice.
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Sustained use discounts applied automatically to long-running VMs
Why it's wrong here
Sustained use discounts (SUDs) automatically apply to VM instances that run more than 25% of a billing month, increasing with usage up to a 30% max discount. A nightly 4-hour batch job runs roughly 15-20% of the month, so it may fall below the 25% threshold entirely, or at best receive only a marginal discount on a few hours. Even if the job partially qualified, SUDs offer significantly less savings (up to 30%) than Spot VMs (up to 91%), making this a weaker cost optimization for an interruptible batch workload.
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Standard on-demand pricing with no special configuration
Why it's wrong here
Standard on-demand pricing charges a fixed per-second rate with no commitment or interruption risk, making it the most expensive option. For a batch job that is explicitly checkpointable and can tolerate VM preemption, paying full on-demand price offers zero cost optimization. Since Spot VMs provide the same compute capability at a fraction of the cost for interruptible workloads, sticking with on-demand pricing would waste budget without any reliability benefit, as the job is already designed to handle interruptions.

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Google ACE Practice Question: Run a batch processing workload that processes 10…

Why each option matters

Option-by-option breakdown

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