AZ-900 Describe cloud concepts Practice Question
A startup has unpredictable traffic — sometimes thousands of users, sometimes almost none. Which pricing model best fits their needs?
⚠ Common exam trap
Many exam-takers confuse 'pay-as-you-go' with 'fixed pricing' or assume Reserved Instances are always cheaper, forgetting that commitments are only beneficial for steady, predictable workloads, not for highly variable traffic.
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
✓
Consumption-based (pay-as-you-go) pricing
Consumption-based (pay-as-you-go) pricing is ideal for unpredictable workloads because it charges only for the resources actually used, with no upfront commitment. This model scales automatically with demand, so the startup pays for compute and storage only when traffic spikes occur, and incurs minimal cost during idle periods. It aligns perfectly with the elastic nature of cloud computing, where resources can be provisioned and deprovisioned dynamically.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reserved Instances with a 1-year commitment
Why it's wrong here
Reserved Instances require a 1-year or 3-year commitment in exchange for a significant discount, but they lock you into a specific VM family, region, and instance size for the entire term. With unpredictable traffic, you must pay for that reserved capacity every month even if your workload is idle, and any burst beyond the reserved amount is billed at higher pay-as-you-go rates. The risk of overcommitting makes this option suitable only for baseline, predictable workloads, not for fluctuating demand.
- ✓
Consumption-based (pay-as-you-go) pricing
Why this is correct
Consumption-based pricing, also known as "pay-as-you-go," lets you pay only for the compute, storage, and network resources you actually consume, typically billed per second or per hour. When traffic spikes, you automatically scale out more virtual machines and pay proportionally more; when traffic drops, you scale in and pay less. This aligns cost directly with demand, eliminating the need to forecast capacity and making it the ideal model for unpredictable, variable workloads.
- ✗
Dedicated Hosts with annual contracts
Why it's wrong here
Dedicated Hosts are physical servers dedicated solely to your workloads, providing control over server-level maintenance and host affinity, but they require an annual contract with a fixed hourly price. This model cannot adapt to unpredictable traffic because you pay for the entire physical host around the clock, even when your workload is idle. Scaling to meet a traffic spike means deploying additional dedicated hosts and committing to another annual contract, making this option rigid and expensive for variable demand.
- ✗
Fixed monthly flat-rate pricing
Why it's wrong here
A fixed monthly flat-rate pricing model charges the same amount regardless of actual resource usage, similar to an old-style leased server or an unlimited hosting plan. With unpredictable traffic, you must set that rate high enough to cover peak-load capacity, so you overpay during quiet periods, or you risk underprovisioning and poor performance during bursts. Cloud services like Azure are metered based on consumption rather than a flat fee, so this option misrepresents the elasticity that cloud computing offers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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