Jeff’s Note #
Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Site Reliability Engineer (SRE).
For SOA-C02 candidates, the confusion often lies in when and how to trigger capacity changes in Auto Scaling groups for predictable versus reactive load. In production, this is about knowing exactly the distinctions between scheduled scaling, dynamic scaling policies, and cooldown periods—and their operational impact. Let’s drill down.
The Certification Drill (Simulated Question) #
Scenario #
ByteWave Technologies runs a web application on multiple Amazon EC2 instances managed by an Auto Scaling group. Users report slow responses every weekend evening between 6 PM and 11 PM. The SRE team needs to improve performance during these predictable peak hours by ensuring sufficient capacity is available ahead of time, while minimizing operational effort and avoiding unnecessary costs.
The Requirement: #
Identify the most operationally efficient solution to handle weekend evening traffic spikes.
The Options #
- A) Create a scheduled Amazon EventBridge rule that triggers an AWS Lambda function to increase the desired capacity before peak hours.
- B) Configure a scheduled scaling action with recurring options to adjust the desired capacity before and after peak hours.
- C) Create a target tracking scaling policy that adds instances when memory utilization exceeds 70%.
- D) Set a cooldown period on the Auto Scaling group and manually modify desired capacity before and after peak hours.
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Correct Answer #
B
Quick Insight: The SOA-C02 Imperative #
- Effective operations means automating scale adjustments with native Auto Scaling scheduled actions rather than custom Lambda invocations.
- Target tracking scaling is reactive and will lag behind sudden workload spikes.
- Manual adjustments increase operational overhead and risk errors during busy periods.
Content Locked: The Expert Analysis #
You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?
The Expert’s Analysis #
Correct Answer #
Option B
The Winning Logic #
Scheduled scaling actions are designed to handle predictable load changes with minimal operational overhead. By configuring a recurring schedule to increase and then decrease desired capacity around known peak times, the Auto Scaling group proactively prepares capacity before traffic spikes, preventing latency issues.
- No custom code or Lambda functions required means less complexity and fewer points of failure compared to option A.
- Target tracking scaling (Option C) is reactive and based on metrics that won’t spike until after latency problems occur, which is too late for predictable traffic surges.
- Manual capacity changes (Option D) increase risk of human error and elevated operational load, which violates best practices for automation in SRE.
The Trap (Distractor Analysis): #
- Why not A? Using an EventBridge + Lambda to adjust capacity introduces unnecessary complexity. Auto Scaling scheduled actions natively support recurring schedules and are simpler to maintain.
- Why not C? Target tracking scaling is great for unpredictable traffic but insufficient for planned surges because scaling reacts after a threshold breach, causing slower response during peak.
- Why not D? Manually changing capacity around peak times contradicts the operational efficiency principle and can lead to mistakes during off hours.
The Technical Blueprint #
# Sample CLI commands to create recurring scheduled actions for Auto Scaling group capacity adjustments
# Increase capacity to 10 instances at 5:45 PM every Friday (pre-peak)
aws autoscaling put-scheduled-update-group-action \
--auto-scaling-group-name my-asg \
--scheduled-action-name IncreaseCapacityBeforePeak \
--recurrence "0 45 17 ? * FRI" \
--desired-capacity 10
# Decrease capacity back to 4 instances at 11:15 PM every Friday night (post-peak)
aws autoscaling put-scheduled-update-group-action \
--auto-scaling-group-name my-asg \
--scheduled-action-name DecreaseCapacityAfterPeak \
--recurrence "0 15 23 ? * FRI" \
--desired-capacity 4
The Comparative Analysis #
| Option | Operational Overhead | Automation Level | Impact on Performance |
|---|---|---|---|
| A | Moderate (requires Lambda) | Medium (custom function) | Pre-scaling possible but complex |
| B | Low (native scheduled action) | High (built-in scaling) | Proactive, best for predictable load |
| C | Low (auto scaling policy) | High (dynamic scaling) | Reactive, may lag during surges |
| D | High (manual intervention) | Low | Risk of late scaling, errors |
Real-World Application (Practitioner Insight) #
Exam Rule #
For the exam, always pick scheduled scaling over Lambda-driven changes when handling predictable, recurring load surges.
Real World #
In real environments, you might combine scheduled scaling with dynamic target tracking policies to cover both predictable and unpredictable traffic fluctuations efficiently.
(CTA) Stop Guessing, Start Mastering #
Disclaimer
This is a study note based on simulated scenarios for the SOA-C02 exam.