DOP-C02 Question 329
Select 3Your organization is running a distributed application on Amazon ECS using the Fargate launch type. The application has unpredictable traffic patterns, which sometimes lead to scaling issues. As a DevOps Engineer, you are tasked with optimizing the application’s scalability while ensuring cost-efficiency. Which combination of actions would help you achieve this goal?
- A
Configure an Application Auto Scaling policy that adjusts the number of ECS tasks based on CPU utilization.
- B
Switch from the Fargate launch type to the EC2 launch type to better control resource allocation.
- C
Use AWS CloudWatch Alarms to track memory usage and trigger scaling events.
- D
Enable Amazon ECS Service Auto Scaling and use a target tracking scaling policy based on request count per target.
- E
Leverage AWS Lambda to monitor ECS service health and manually scale tasks when needed.
Show answer and explanation
Correct answers: A, C, D
Explanation
To optimize ECS scalability with unpredictable traffic, it is essential to use automated scaling solutions like Application Auto Scaling and ECS Service Auto Scaling with relevant metrics (e.g., CPU, memory, or request count). These methods provide responsive and cost-efficient scaling. While Lambda can monitor health, it is suboptimal for automated scaling. Switching to EC2 increases complexity and operational costs, which is contrary to the goal of cost-efficiency.
- A. Correct.
Using an Application Auto Scaling policy based on CPU utilization aligns with unpredictable traffic patterns, as scaling is triggered by real-time resource demand.
- B. Incorrect.
Switching to the EC2 launch type might give more control over resource allocation but increases operational overhead and reduces the benefit of serverless Fargate.
- C. Correct.
Tracking memory usage and triggering scaling events with CloudWatch Alarms ensures that scaling decisions consider more than just CPU usage, which is critical for applications with variable traffic patterns.
- D. Correct.
ECS Service Auto Scaling with a target tracking policy based on request counts allows for scaling decisions to be made based on dynamic traffic, enhancing responsiveness and cost-efficiency.
- E. Incorrect.
Using AWS Lambda to manually scale tasks is not an automated solution and introduces delays and potential errors, which is not ideal for unpredictable traffic.