Auto Scaling ECS Fargate Services
A company is designing a new microservices application on AWS. Each microservice will be deployed as a containerized application using Amazon ECS with Fargate launch type. The company expects variable traffic patterns and needs to ensure that the application can scale automatically based on demand. Which scaling solution should be used?
Quick Answer
The correct choice is to configure Application Auto Scaling with a target tracking scaling policy based on average CPU utilization. This solution works because Amazon ECS with the Fargate launch type relies on Application Auto Scaling to dynamically adjust the desired task count, and a target tracking policy maintains a specified metric—such as 70% average CPU—by automatically adding or removing tasks in response to demand. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of how to implement auto scaling for ECS Fargate services under variable traffic patterns, often contrasting it with scheduled scaling or step scaling policies. A common trap is selecting a simple CloudWatch alarm-based step scaling policy, which requires manual threshold tuning and can react too aggressively. Remember the memory tip: “Track the target, not the steps”—target tracking policies are the simplest and most effective way to auto scale ECS Fargate services based on steady-state utilization metrics.
⚠ Common exam trap
Many candidates confuse EC2 Auto Scaling (which manages instances) with Application Auto Scaling (which manages ECS tasks), leading candidates to choose Option A despite Fargate being serverless and not requiring EC2 instance management.
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
✓
Configure Application Auto Scaling with a target tracking scaling policy based on average CPU utilization.
Amazon ECS with Fargate uses Application Auto Scaling to automatically adjust the desired count of tasks based on demand. A target tracking scaling policy based on average CPU utilization is the correct approach because it allows you to define a target value (e.g., 70% CPU) and Application Auto Scaling will add or remove tasks to maintain that target, matching the variable traffic patterns described.
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 Amazon EC2 Auto Scaling to add more Fargate tasks.
Why it's wrong here
EC2 Auto Scaling manages EC2 instances, not Fargate tasks; ECS Service Auto Scaling is needed.
- ✓
Configure Application Auto Scaling with a target tracking scaling policy based on average CPU utilization.
Why this is correct
Application Auto Scaling with target tracking is the standard method to automatically scale ECS services based on a metric like CPU.
- ✗
Use AWS Auto Scaling Plans with predictive scaling.
Why it's wrong here
Predictive scaling requires historical traffic data spanning at least 14 days to generate forecasts, but the scenario describes a new application with variable patterns and no existing usage history, so the algorithm cannot build a baseline. It is tempting because predictive scaling proactively provisions capacity ahead of demand spikes, which would be correct for a mature application with stable, repeating traffic cycles.
- ✗
Manually adjust the desired count of tasks in the ECS service based on traffic analysis.
Why it's wrong here
Manual scaling is not automated and does not meet the requirement for automatic scaling.
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Same concept, more angles
2 more ways this is tested on SAP-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is designing a multi-tier web application on AWS. They want to ensure that the web tier can scale automatically based on CPU utilization. Which AWS service should they use?
medium- A.Amazon CloudFront
- B.Amazon Route 53
- ✓ C.Auto Scaling groups
- D.Elastic Load Balancing
Why C: Auto Scaling groups (Option C) are the correct service because they directly manage the automatic scaling of EC2 instances based on defined policies, such as a target CPU utilization threshold. When CPU utilization exceeds the threshold, the Auto Scaling group launches new instances to handle the load, and it terminates instances when utilization drops, ensuring the web tier scales automatically.
Variation 2. A company is designing a multi-tier web application that must be fault-tolerant and scalable. The application uses an Application Load Balancer (ALB) to distribute traffic to EC2 instances in an Auto Scaling group. The instances run a web server and a backend application. Which TWO steps should be taken to ensure the application can scale without data loss?
medium- A.Use instance store volumes for temporary data.
- ✓ B.Store session state in an external data store such as ElastiCache.
- ✓ C.Implement lifecycle hooks to gracefully handle instance termination.
- D.Use a custom CloudWatch metric to scale based on CPU utilization.
- E.Use a fixed number of EC2 instances instead of Auto Scaling.
Why B: Storing session state in an external data store like ElastiCache decouples session data from individual EC2 instances. This ensures that if an instance is terminated or replaced during scaling events, the session data persists and can be served by any other instance in the Auto Scaling group, preventing data loss and maintaining user experience.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAP-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 SAP-C02 exam.