MS-900 Describe cloud concepts Practice Question
An e-commerce application runs in the cloud and automatically adjusts the number of virtual machines based on real-time traffic. When traffic spikes, more VMs are added; when traffic drops, VMs are removed. Which cloud computing characteristic does this behavior exemplify?
⚠ Common exam trap
Many candidates confuse 'on-demand self-service' (manual provisioning by the user) with 'rapid elasticity' (automatic scaling), because both involve responding to demand, but only elasticity handles real-time, automated adjustments without user intervention.
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
✓
Rapid elasticity
Rapid elasticity is the cloud characteristic that enables resources to scale out (add VMs) and scale in (remove VMs) automatically in response to real-time demand. In this e-commerce scenario, the application adjusts VM count based on traffic spikes and drops, which directly matches the definition of rapid elasticity as defined by NIST SP 800-145.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Rapid elasticity
Why this is correct
Rapid elasticity is the cloud characteristic that lets an e-commerce application dynamically add or remove compute capacity—such as VM instances or containers—in near-real time as traffic spikes or falls. An autoscaling group monitors metrics like CPU utilization or request count and adjusts resources automatically, so the application always has enough capacity without manual intervention. This exactly matches the scenario's automatic scaling based on demand.
- ✗
Measured service
Why it's wrong here
Measured service means the cloud provider meters and monitors resource consumption—compute hours, storage, network I/O—for billing and usage analysis, typically on a pay-per-use basis. Although auto-scaling will change the measured usage and therefore the bill, the metering mechanism itself neither triggers nor performs the scaling action. The scenario describes the behavior of the workload, not the telemetry/billing model that tracks its consumption.
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Resource pooling
Why it's wrong here
Resource pooling is a provider-side architectural model in which physical servers, storage, and network equipment are virtualized and shared among multiple tenants, giving the provider economies of scale. This explains why a cloud can host many e-commerce applications on the same hardware, but it does not describe how one application's capacity expands and contracts in response to demand. The pool is the source of available resources; elasticity is the mechanism that draws from the pool on demand.
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On-demand self-service
Why it's wrong here
On-demand self-service is the ability for an administrator to provision or deprovision resources—like spinning up a new VM or resizing a database—via a web portal or API without human involvement from the provider. This is a one-time request-driven action, not an automatic, policy-driven adjustment based on current load. The e-commerce application may have been initially provisioned through self-service, but the scenario's continuous scaling in response to demand is fundamentally elasticity.
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