How do custom metrics contribute to performance optimization in AWS?
- Automate scaling
- Identify bottlenecks
- Improve fault tolerance
- Streamline deployment
Custom metrics contribute to performance optimization in AWS by helping identify bottlenecks, enabling automated scaling, and providing insights for proactive optimization strategies.
__________ is an AWS service commonly used for storing custom metrics data.
- AWS Lambda
- Amazon CloudWatch
- Amazon DynamoDB
- Amazon Redshift
Amazon CloudWatch is commonly used for storing custom metrics data in AWS.
AWS provides __________ for creating dashboards and visualizations of custom metrics.
- AWS Glue
- AWS QuickSight
- AWS Step Functions
- Amazon CloudWatch Dashboards
AWS provides Amazon CloudWatch Dashboards for creating dashboards and visualizations of custom metrics.
__________ allows you to set alarms and triggers based on custom metrics thresholds in AWS.
- AWS CloudTrail
- AWS Config
- Amazon CloudWatch
- Amazon SNS
Amazon CloudWatch allows you to set alarms and triggers based on custom metrics thresholds, providing detailed monitoring and observability.
Custom metrics are valuable for monitoring __________ in AWS environments.
- Application performance
- Billing and costs
- Resource tags
- User activity
Custom metrics help in monitoring application performance by providing specific insights into application behavior and health.
Implementing custom metrics helps in gaining insights into __________ in AWS services.
- Data encryption
- Resource utilization
- Service level agreements (SLAs)
- User authentication
Implementing custom metrics provides detailed insights into resource utilization, helping optimize performance and costs.
What is the purpose of error handling in AWS Lambda?
- To automatically retry failed executions
- To gracefully manage runtime errors
- To increase function execution time
- To reduce the function's memory usage
Error handling in AWS Lambda is essential to gracefully manage runtime errors, ensuring that they are properly logged and handled without crashing the application.
Which AWS service is commonly used for logging AWS Lambda function output?
- AWS CloudTrail
- AWS Config
- Amazon CloudWatch
- Amazon S3
Amazon CloudWatch is commonly used for logging AWS Lambda function output, providing monitoring and logging capabilities for AWS resources and applications.
What happens when an error occurs within an AWS Lambda function?
- The error is ignored
- The function continues executing
- The function execution is halted and the error is logged
- The function retries automatically
When an error occurs within an AWS Lambda function, the function execution is halted and the error is logged, allowing for debugging and error handling strategies to be implemented.
How can you configure error handling in AWS Lambda functions?
- All options are correct
- Use AWS Lambda Destinations
- Use Dead Letter Queues
- Use Retries and Timeouts
AWS Lambda provides multiple ways to configure error handling, including AWS Lambda Destinations, Dead Letter Queues, and configuring retries and timeouts.