DOP-C02 Resilient Cloud Solutions Practice Question
A company runs a critical web application on AWS using an Application Load Balancer (ALB) in front of an Auto Scaling group of EC2 instances. The application experiences periodic traffic spikes. To handle these spikes, the company wants to use a combination of proactive scaling based on a predictable schedule and reactive scaling based on CPU utilization. What is the MOST resilient scaling strategy?
⚠ Common exam trap
Watch out — candidates often assume predictive scaling (Option B) is the best for all predictable patterns, but it fails for non-recurring or sudden spikes, and they overlook that target tracking (Option D) cannot proactively add capacity before a spike begins.
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
✓
Use a scheduled scaling policy for the predictable spikes and a step scaling policy for CPU utilization.
It combines scheduled scaling for predictable traffic spikes with step scaling for reactive adjustments based on CPU utilization, providing both proactive and reactive resilience. Scheduled scaling adjusts capacity in advance of known events, while step scaling allows for larger, more aggressive adjustments when CPU utilization exceeds thresholds, avoiding the slower, linear response of target tracking. This dual approach ensures the application can handle spikes without over-provisioning or under-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.
- ✓
Use a scheduled scaling policy for the predictable spikes and a step scaling policy for CPU utilization.
Why this is correct
Scheduled scaling pre-provisions instances at fixed times, aligning capacity with known upcoming demand, while a step scaling policy reacts to actual CPU utilization through CloudWatch alarms with defined adjustment steps (e.g., add 2 instances when CPU exceeds 80%, add 1 when it exceeds 70%). This combination ensures both proactive readiness for predictable traffic spikes and rapid reactive response to any deviation, giving the highest resilience for mixed traffic patterns.
- ✗
Use predictive scaling based on historical traffic patterns.
Why it's wrong here
Predictive scaling uses machine learning on historical load data to forecast future capacity needs, but it relies on at least 14 days of representative data and assumes recurring patterns. If a flash crowd, viral event, or business change causes a pattern the model hasn't seen, forecasts will be incorrect, and because predictive scaling is purely time-based, it cannot trigger on live CPU spikes, making it insufficient for critical applications needing immediate reaction to anomalies.
- ✗
Use manual scaling by increasing the desired capacity before expected spikes.
Why it's wrong here
Manual scaling requires an operator to adjust desired capacity or update the Auto Scaling group before each spike, which is time-consuming and error-prone, especially for unexpected events. It offers no automation, so if the operator is unavailable or the spike timing shifts, the application lacks capacity; additionally, manual changes are not reverted automatically, requiring ongoing human oversight to remove excess instances and avoid over-provisioning.
- ✗
Use a target tracking scaling policy based on average CPU utilization.
Why it's wrong here
A target tracking policy maintains a fixed CloudWatch metric target (e.g., CPU at 50%) by adjusting capacity proportionally, but it only reacts after the metric has breached the target over the alarm evaluation period, so it is inherently lagging for sudden spikes. Its coarse single-metric control cannot apply multiple thresholds or aggressive step adjustments, and CPU averages can mask short bursts of high utilization, causing slower scale-out than a dedicated step scaling policy.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DOP-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DOP-C02 exam.