AZ-900 Describe cloud concepts Practice Question
What is 'scalability' in the context of cloud computing?
⚠ Common exam trap
Candidates often confuse scalability with high availability or disaster recovery, as all three are cloud benefits but serve distinct purposes; candidates often pick 'keeping services running during failures' (A) because they associate 'scaling' with reliability, but scalability is specifically about matching demand, not fault tolerance.
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
✓
The ability to increase or decrease resources to match demand
Scalability in cloud computing refers to the ability to dynamically adjust resources (such as compute power, memory, or storage) to match fluctuating demand. This is a core benefit of cloud platforms like Azure, enabling automatic scaling via services such as Azure Virtual Machine Scale Sets or Azure App Service autoscale, ensuring performance without over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The ability to keep services running during failures
Why it's wrong here
Keeping services running during failures is the essence of high availability and fault tolerance. These capabilities rely on redundancy, failover clustering, and availability zones to minimize downtime when hardware or software components fail. However, ensuring continuous operation during failures has nothing to do with adjusting resource capacity to match current workload demand. Thus, this option describes resilience, not scalability.
- ✓
The ability to increase or decrease resources to match demand
Why this is correct
Scalability is the ability to increase or decrease resources in response to workload demand, which is the precise definition of the term. In Azure, this can be achieved vertically by resizing a VM to a larger or smaller SKU, or horizontally by adding or removing instances behind a load balancer. Azure Autoscale can automatically perform these adjustments based on metrics like CPU usage or queue length. This dynamic provisioning of resources is exactly what the correct answer states.
- ✗
The ability to recover data after a disaster
Why it's wrong here
Recovering data after a disaster is the domain of disaster recovery (DR), which focuses on restoring business operations using backups, replication, and failover mechanisms. DR strategies are concerned with recovery time objectives (RTO) and recovery point objectives (RPO), not with reacting to real-time demand changes. This option describes a post-incident recovery capability, whereas scalability is a proactive, continuous adjustment of resources to meet prevailing load conditions.
- ✗
The ability to deploy resources in multiple geographic regions
Why it's wrong here
Deploying resources in multiple geographic regions is a strategy for global reach, latency reduction, or geo-redundancy to protect against regional outages. This placement decision is about where resources are located, not about how their capacity is adjusted based on demand. While multi-region deployment can support global scale, the capability to increase or decrease resources to match demand is a separate operational characteristic. Therefore, this option relates to geo-distribution, not scalability.
Go deeper
Related to this question
Learn chapter
What is Cloud Computing?
Key term
Azure virtual machine
An Azure virtual machine is a software-based emulation of a physical computer that runs in Microsoft's cloud, allowing you to deploy and manage applications without owning the hardware.
Key term
Scalability
Scalability is the ability of a system, network, or process to handle a growing amount of work by adding resources, either by making the existing resources more powerful (vertical scaling) or by adding more resources (horizontal scaling).
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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