A company runs a stateless application tier behind an Application Load Balancer. Match each observed scaling pattern on the left to the best Auto Scaling strategy or metric on the right.
Drag a concept onto its matching description — or click a concept then click the description.
Scale the Auto Scaling group on ALB RequestCountPerTarget.
Scale on SQS queue depth using a custom CloudWatch metric.
Use scheduled scaling to add capacity before the recurring surge.
Use target tracking on EC2 CPUUtilization.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Steady increase: Step scaling
Steady increase is best handled by step scaling for gradual adjustments; sudden spikes use simple scaling for immediate action; cyclical patterns benefit from scheduled scaling; consistent low traffic may not need scaling; unpredictable bursts are managed by target tracking to maintain a metric; gradual decrease uses simple scaling to reduce capacity.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Steady increase: Step scaling
Why this is correct
Step scaling policies define multiple thresholds, each associated with a specific capacity adjustment. As a metric like CPU utilization gradually crosses these thresholds, the Auto Scaling group incrementally adds or removes instances in 'steps,' providing a controlled and responsive adjustment for steady, predictable growth patterns. This prevents drastic over- or under-provisioning during sustained load changes.
Sudden spikes: Simple scaling
Why this is correct
Simple scaling policies trigger an immediate, fixed capacity adjustment when a single metric threshold is breached. While effective for reacting quickly to sudden, short-lived spikes in demand, these policies enforce a cooldown period after scaling activities. This cooldown prevents rapid, consecutive scaling actions, making them less suitable for continuously fluctuating loads but ideal for immediate, reactive bursts.
Cyclical patterns: Scheduled scaling
Why this is correct
Scheduled scaling policies allow you to define specific times and dates for your Auto Scaling group to scale up or down. By utilizing cron expressions or specific time windows, capacity can be proactively adjusted to match predictable, recurring traffic patterns, such as daily peak hours or weekly business cycles. This ensures resources are available precisely when needed, optimizing cost and performance for known demand fluctuations.
Unpredictable bursts: Target tracking
Why this is correct
Target tracking scaling policies dynamically adjust the Auto Scaling group's capacity to maintain a specified target value for a chosen metric, such as average CPU utilization or request count per target. It proactively scales out or in as needed to keep the metric close to the target, making it highly effective for handling unpredictable and fluctuating workloads. This approach ensures optimal performance without requiring manual intervention for unforeseen demand changes.
Steady increase: Simple scaling
Why it's wrong here
Simple scaling is inappropriate for steady increases because its fixed capacity adjustments and mandatory cooldown periods lead to inefficient resource management for gradual changes. It either over-provisions significantly after a single threshold breach or under-provisions during the cooldown, failing to smoothly match a steadily growing demand. This results in suboptimal cost efficiency and potential performance degradation over time.
Unpredictable bursts: Scheduled scaling
Why it's wrong here
Scheduled scaling is unsuitable for unpredictable bursts because it relies entirely on pre-defined time-based rules, which cannot react to unforeseen or spontaneous increases in demand. Since it lacks real-time metric evaluation, it would fail to provision additional capacity when an unexpected burst occurs, leading to performance bottlenecks or service unavailability. This policy is designed for known patterns, not dynamic, real-time reactions.
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Same concept, more angles
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Variation 1. A company runs a stateless application tier behind an Application Load Balancer. Match each observed scaling pattern on the left to the best Auto Scaling strategy or metric on the right.
hardWhy A: Steady increase uses step scaling for gradual adjustments; sudden spikes are best handled by step scaling; cyclical patterns use scheduled scaling; consistent low traffic may need no scaling; unpredictable bursts use target tracking; gradual decrease uses simple scaling.
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAA-C03 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 SAA-C03 exam.