An application team uses an Amazon Aurora MySQL DB cluster with one Aurora Replica.

Step-by-Step Explanation:

Understand the Problem:

The application read performance degrades when user connections exceed 200.

Need to automatically scale the database based on user demand.

Analyze the Requirements:

Implement auto scaling based on user connections to ensure optimal performance.

Evaluate the Options:

Option A: Migrate to a new Aurora multi-master DB cluster.

Multi-master clusters provide read and write scalability but may require significant changes to the application.

Option B: Modify the DB cluster to serverless mode.

Aurora Serverless provides automatic scaling, but it is more suitable for variable workloads with unpredictable demand.

Option C: Create an auto scaling policy with a target metric of 195 DatabaseConnections.

This directly addresses the need to scale based on user connections.

Auto scaling can add or remove replicas to maintain optimal performance.

Option D: Increase the Aurora Replica instance size.

This may improve performance but does not address scalability for sudden increases in user connections.

Select the Best Solution:

Option C: Creating an auto scaling policy with a target metric of 195 DatabaseConnections ensures the DB cluster scales automatically to handle increased load.

Amazon Aurora Auto Scaling

Scaling Aurora DB Instances

Auto scaling based on DatabaseConnections ensures the Aurora DB cluster maintains optimal performance as user demand fluctuates.