How can you visualize custom metrics in AWS?
- Use AWS Lambda functions
- Use Amazon CloudWatch dashboards
- Use Amazon RDS instances
- Use Amazon S3 buckets
You can visualize custom metrics in AWS by using Amazon CloudWatch dashboards, which allow you to create custom widgets to monitor and analyze your data effectively.
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.
By enabling __________ in AWS Lambda, you can ensure that logs are retained for future analysis.
- Auto-scaling
- Environment variables
- Log retention policies
- VPC
Enabling log retention policies in AWS Lambda ensures that logs are retained for a specified period, allowing for future analysis.
AWS CloudTrail can be used to provide a detailed record of API calls made to __________ services.
- AWS
- Google Cloud
- Microsoft Azure
- On-premises
AWS CloudTrail provides a detailed record of API calls made to AWS services, helping with monitoring and compliance.
Implementing __________ in AWS Lambda can help detect and alert on abnormal behavior or errors.
- AWS Config
- AWS Elastic Beanstalk
- AWS Step Functions
- Amazon CloudWatch Logs
Amazon CloudWatch Logs can be used in AWS Lambda to monitor, detect, and alert on abnormal behavior or errors by capturing and analyzing log data.
__________ in AWS Lambda allows you to define custom error handling logic and responses.
- AWS CloudFormation
- AWS IAM
- AWS Lambda Destinations
- AWS X-Ray
AWS Lambda Destinations enable you to define custom actions and error handling logic for successful or failed asynchronous invocations.
Scenario: You're tasked with troubleshooting performance issues in an AWS Lambda function. How would you utilize CloudWatch Logs and metrics to identify and resolve errors?
- Analyze CloudWatch Logs for error messages and stack traces
- Enable AWS X-Ray for detailed tracing of function execution
- Set up CloudWatch Alarms to notify you of performance thresholds
- Use CloudWatch Metrics to monitor function duration and memory usage
Analyzing CloudWatch Logs for error messages and stack traces helps pinpoint specific errors and performance issues within the AWS Lambda function.
Scenario: You're designing an error handling strategy for a high-throughput AWS Lambda application. Discuss how you would implement automated alerting and remediation for critical errors.
- Configure Step Functions for automated retries and fallback actions
- Implement Lambda Destinations for asynchronous error handling
- Set up EventBridge rules to capture and respond to specific error patterns
- Use CloudWatch Alarms to trigger SNS notifications for critical errors
Using CloudWatch Alarms to trigger SNS notifications ensures that critical errors are promptly communicated, allowing for quick remediation.
Which AWS feature can be used to trigger actions based on specific error patterns in AWS Lambda?
- CloudWatch Alarms
- CodePipeline
- SNS Notifications
- Step Functions
CloudWatch Alarms can be set to trigger actions when specific error patterns or thresholds are met, allowing for automated response to errors.
How does AWS X-Ray facilitate error tracing in AWS Lambda functions?
- By creating backups of function data
- By encrypting function code
- By providing end-to-end request tracking
- By storing logs in CloudWatch
AWS X-Ray provides end-to-end request tracking, which helps in tracing errors and performance bottlenecks in AWS Lambda functions by showing a map of the request's path through the services.