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A company runs an ecommerce web application on AWS.

A company runs an ecommerce web application on AWS. The web application is hosted as a static website on Amazon S3 with Amazon CloudFront for content delivery. An Amazon API Gateway API invokes AWS Lambda functions to handle user requests and order processing for the web application. The Lambda functions store data in an Amazon RDS for MySQL DB cluster that uses On-Demand Instances. The DB cluster usage has been consistent in the past 12 months. Recently, the website has experienced SQL injection and web exploit attempts. Customers also report that order processing time has increased during periods of peak usage. During these periods, the Lambda functions often have cold starts. As the company grows, the company needs to ensure scalability and low-latency access during traffic peaks. The company also must optimize the database costs and add protection against the SQL injection and web exploit attempts. Which solution will meet these requirements?

A.

Configure the Lambda functions to have an increased timeout value during peak periods. Use RDS Reserved Instances for the database. Use CloudFront and subscribe to AWS Shield Advanced to protect against the SQL injection and web exploit attempts.

B.

Increase the memory of the Lambda functions. Transition to Amazon Redshift for the database. Integrate Amazon Inspector with CloudFront to protect against the SQL injection and web exploit attempts.

C.

Use Lambda functions with provisioned concurrency for compute during peak periods. Transition to Amazon Aurora Serverless for the database. Use CloudFront and subscribe to AWS Shield Advanced to protect against the SQL injection and web exploit attempts.

D.

Use Lambda functions with provisioned concurrency for compute during peak periods. Use RDS Reserved Instances for the database. Integrate AWS WAF with CloudFront to protect against the SQL injection and web exploit attempts.

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