Serverless Computing

Correct Answer: Use AWS API Gateway to validate query parameters before invoking serverless functions.

The best method for securely handling query parameters in a serverless API is to use AWS API Gateway to validate the parameters before invoking the serverless functions. API Gateway provides built-in features for request validation, allowing you to define validation rules for query parameters. This ensures that only valid and expected parameters are passed to the serverless functions, reducing the risk of injection attacks or unexpected behavior. Parsing query parameters directly in the function can be insecure if proper validation and sanitization are not implemented. Storing query parameters in environment variables is not a secure approach as they can be accessed by other functions or compromised. Using a custom authentication provider to validate query parameters adds complexity and may not provide the same level of built-in validation capabilities as API Gateway.

Correct Answer: Increase memory allocation for the Lambda function

The performance of AWS Lambda functions is directly tied to the amount of memory that you allocate to them. When you increase memory, CPU power, network bandwidth and disk I/O will proportionally increase as well. Hence, increasing the memory allocation will speed up the image processing of the Lambda function, addressing the slow performance issue. On the other hand, adding more EC2 instances or creating a bigger S3 bucket doesn't directly impact the performance of a Lambda function as they are separate AWS services. Changing the programming language also isn't generally a quick or effective solution for performance issues as they normally depends on the design pattern or the correct utilization of AWS resources.

Correct Answer: Implement API Gateway and set request throttling limits

'Implement API Gateway and set request throttling limits' is the best approach for handling the cost management of a serverless application for image processing. API Gateway provides throttling rules to manage the rate of incoming requests to prevent your backend from being overwhelmed, which can help manage costs by limit the number of processed requests. This approach allows you to manage the load on your infrastructure without the need to manually intervene every time there's a spike in requests. 'Use manual intervention to control the requests' would consume substantial resources and time and also would not be practical in real time scenarios. 'Remove IAM role permissions' would not be suitable because it might disrupt other processes which use the same permissions. 'Reduce the number of AWS Lambda Functions' could possibly impact the overall function of the serverless application as Lambda Functions are isolated computational units that enable the application to function in a distributed manner.