- A
Assume the autoscaling group always runs at average load (10 VMs) and multiply by the annual hours to get the new cost
Why wrong: 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.
- B
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
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.
- C
Request a custom quote from Google Cloud sales since pricing for autoscaling groups is negotiated individually
Why wrong: Autoscaling VMs are billed at the same published per-VM-hour rates as on-demand VMs. No custom quote is needed — the pricing calculator handles this calculation directly.
- D
The cost will be identical since autoscaling groups use the same VM type as the fixed fleet
Why wrong: The VM type is the same, but the number of VMs running at any given time is different. Autoscaling reduces average running instances significantly compared to a fixed fleet, directly reducing costs.
How to Estimate Cost Impact of Autoscaling vs Fixed VMs in Google Cloud
This GCDL practice question tests your understanding of scaling with google cloud operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Quick Answer
The correct analytical approach is to model the actual usage pattern by calculating the daily cost for 16 normal hours at 10 VMs plus 8 peak hours at 50 VMs, then comparing that to the fixed cost of 50 VMs running 24/7, using the Google Cloud Pricing Calculator for the n2-standard-4 instance type. This is correct because autoscaling does not change per-VM pricing; it reduces total cost by aligning resource consumption with demand, so you must account for the variable number of instances over time rather than assuming a constant average. On the Google Cloud Digital Leader exam, this tests your understanding of pay-per-use billing and how to estimate cost impact of autoscaling vs fixed VMs in Google Cloud—a common trap is simply averaging the VM count (e.g., 30 VMs) instead of weighting by hours. Memory tip: think “weighted hours, not flat averages”—break the day into distinct load periods and multiply each by its VM count before summing.
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Request a custom quote from Google Cloud sales since pricing for autoscaling groups is negotiated individually
Why it's wrong here
Autoscaling VMs are billed at the same published per-VM-hour rates as on-demand VMs. No custom quote is needed — the pricing calculator handles this calculation directly.
- ✗
The cost will be identical since autoscaling groups use the same VM type as the fixed fleet
Why it's wrong here
The VM type is the same, but the number of VMs running at any given time is different. Autoscaling reduces average running instances significantly compared to a fixed fleet, directly reducing costs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, Google Compute Engine bills per second for on-demand VMs with a 1-minute minimum, so the autoscaling group's cost is directly proportional to the total VM-seconds consumed. The n2-standard-4 instance has a specific per-hour rate in the Google Cloud Pricing Calculator, and autoscaling does not introduce any additional fees or discounts. In a real-world scenario, if the peak load varies day-to-day, you would need to model the actual scaling events and use sustained-use discounts or committed use contracts for further optimization.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this GCDL question test?
Scaling with Google Cloud operations — This question tests Scaling with Google Cloud operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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 — Option B is correct because 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.
What should I do if I get this GCDL question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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