Cloud Digital Leader Scaling with Google Cloud operations Practice Question

An operations team has been asked to estimate the annual cost impact of a proposed new cloud architecture. The architecture would replace 50 on-demand n2-standard-4 VMs (running 24/7) with an autoscaling group that averages 10 VMs under normal load but scales to 50 during peak hours (approximately 8 hours per day). Which analytical approach best estimates the cost impact?

⚠ Common exam trap

Many exam-takers assume autoscaling changes the per-VM pricing or requires special negotiation, when in fact it simply adjusts the number of running instances, and the cost impact is purely a function of total VM-hours at the standard on-demand rate.

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

✓

Model the actual usage pattern: calculate cost for (16 normal hours × 10 VMs) + (8 peak hours × 50 VMs) per day, compare to fixed cost of 50 VMs × 24 hours, and use Google Cloud Pricing Calculator to price the VM type

It accurately models the variable usage pattern of the autoscaling group: 16 hours at 10 VMs plus 8 peak hours at 50 VMs per day. This approach then compares the daily cost to the fixed 50 VMs × 24 hours baseline, using the Google Cloud Pricing Calculator to price the n2-standard-4 instance type. This reflects the pay-per-use billing model of Google Compute Engine, where autoscaling does not change per-VM pricing but reduces total cost by running fewer instances during off-peak hours.

Answer analysis

Option-by-option breakdown

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

✗
Assume the autoscaling group always runs at average load (10 VMs) and multiply by the annual hours to get the new cost
Why it's wrong here
Using only the average ignores peak hours when 50 VMs run. This underestimates cost. The model must account for both normal-load hours (16 hours/day × 10 VMs) and peak hours (8 hours/day × 50 VMs) separately.
✓
Model the actual usage pattern: calculate cost for (16 normal hours × 10 VMs) + (8 peak hours × 50 VMs) per day, compare to fixed cost of 50 VMs × 24 hours, and use Google Cloud Pricing Calculator to price the VM type
Why this is correct
This is the correct approach. Per day: 16 × 10 = 160 VM-hours (normal) + 8 × 50 = 400 VM-hours (peak) = 560 VM-hours. Fixed: 50 × 24 = 1,200 VM-hours. Autoscaling uses 53% fewer VM-hours. Pricing Calculator gives the $/VM-hour to calculate actual dollar savings.
✗
Request a custom quote from Google Cloud sales since pricing for autoscaling groups is negotiated individually
Why it's wrong here
Autoscaling groups are billed the same as any other VM at the published on-demand per-second rates; there is no negotiated price for autoscaling itself. A custom quote would only be relevant for enterprise discounts, committed use contracts, or custom VM configurations, not for standard autoscaling usage. The Google Cloud Pricing Calculator already accepts VM type, region, and runtime hours, making it the correct way to price the 560 VM-hours/day autoscaling pattern. This estimate is directly comparable to the fixed-fleet cost without needing sales engagement.
✗
The cost will be identical since autoscaling groups use the same VM type as the fixed fleet
Why it's wrong here
The claim that cost is identical conflates per-instance price with total run-time cost. While the VM type is the same, autoscaling changes the number of running instances over time: 10 VMs for 16 hours and 50 VMs for 8 hours yields 560 VM-hours per day, versus 1,200 VM-hours for a fixed 50-VM fleet. At any per-VM-hour rate, the autoscaling fleet costs about 53% less because it runs far fewer cumulative VM-hours.

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Cloud Digital Leader Scaling with Google Cloud operations Practice Question

Why each option matters

Option-by-option breakdown

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