SAA-C03 Design Cost-Optimized Architectures Practice Question
A startup expects steady compute usage around the clock for the next year. They want to reduce costs compared to On-Demand pricing, without tightly planning specific instance types. Which option best matches their goal?
⚠ Common exam trap
Watch out — candidates often confuse Compute Savings Plans with Reserved Instances, assuming both lock you to a specific instance type, but Compute Savings Plans provide full flexibility across instance families, sizes, and even compute services.
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
✓
Purchase a Compute Savings Plan to receive discounted rates for a usage amount over a 1-year term.
A Compute Savings Plan offers the lowest prices on EC2 compute usage (including Fargate and Lambda) in exchange for a commitment to a consistent amount of compute (measured in $/hour) over a 1-year or 3-year term. This matches the startup's steady, predictable usage and provides up to 66% savings over On-Demand, while allowing flexibility to change instance families, sizes, regions, or even switch to containers without renegotiating the plan.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Purchase a Compute Savings Plan to receive discounted rates for a usage amount over a 1-year term.
Why this is correct
Compute Savings Plans provide discounted EC2 usage (and related compute usage) versus On-Demand for a committed amount per hour. They are not limited to a single instance type, so the team can change instance families while staying within the committed usage.
- ✗
Purchase a Reserved Instance that must be tied to exactly one specific instance size (no flexibility to switch instance families).
Why it's wrong here
Reserved Instances typically require commitment to attributes like instance family/operating system/tenancy, which limits flexibility. The statement that they must be tied to exactly one instance size with no flexibility is not the key distinction; more importantly, Savings Plans are generally more flexible across instance types than many Reserved Instance choices.
- ✗
Only use Spot Instances and set the workload to stop immediately if capacity is interrupted.
Why it's wrong here
Spot Instances can reduce compute costs by up to 90%, but they are interruptible with only a two-minute warning when EC2 reclaims capacity. A steady 24/7 workload that is not designed to tolerate interruptions would face unexpected downtime, and even with diversified instance pools, there is no guarantee of capacity for the entire term. Thus, Spot alone cannot provide the reliable baseline required for this always-on startup workload, making it an unsuitable cost strategy for the entire compute footprint.
- ✗
Rely on On-Demand pricing and add more alarms to detect when costs spike.
Why it's wrong here
On-Demand pricing offers full flexibility without commitment but is the most expensive EC2 purchasing option, charging per-second rates with no volume discounts. Adding CloudWatch billing alarms or Cost Anomaly Detection only surfaces cost spikes after they occur; these tools do not lower the hourly rate, enforce a budget, or automatically shut down resources. For a steady 24/7 workload, this approach leaves significant savings (typically 30–60%) unclaimed, as the team would pay full price indefinitely while merely responding to cost alerts instead of proactively reducing the incurred charges.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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