How can you collect custom metrics in AWS?
- Use AWS Lambda functions
- Use Amazon CloudWatch custom metrics
- Use Amazon RDS instances
- Use Amazon S3 buckets
Amazon CloudWatch provides a feature to collect custom metrics, allowing you to monitor specific aspects of your applications or services beyond the standard metrics provided by AWS services.
What AWS service is commonly used for storing and analyzing custom metrics?
- Amazon CloudWatch
- Amazon DynamoDB
- Amazon EC2
- Amazon SQS
Amazon CloudWatch is commonly used for storing and analyzing custom metrics in AWS, providing dashboards, alarms, and insights into system performance and behavior.
What are some examples of custom metrics that can be collected in AWS?
- Application latency, API response time, custom error rates
- CPU utilization of Amazon S3 buckets
- Disk space usage of Amazon SQS queues
- Network bandwidth of Amazon RDS instances
Examples of custom metrics that can be collected in AWS include application latency, API response time, and custom error rates, allowing you to monitor and optimize various aspects of your applications or services.
AWS X-Ray provides __________ for analyzing performance trends and identifying anomalies in application behavior.
- Insights
- Logs
- Metrics
- Triggers
AWS X-Ray provides insights for analyzing performance trends and identifying anomalies in application behavior, allowing developers to optimize performance and troubleshoot issues.
__________ is a key AWS service that integrates with AWS X-Ray to provide comprehensive monitoring and analysis capabilities.
- Amazon CloudWatch
- Amazon RDS
- Amazon Redshift
- Amazon S3
Amazon CloudWatch is a key AWS service that integrates with AWS X-Ray to provide comprehensive monitoring and analysis capabilities, allowing you to monitor metrics, collect log files, and set alarms.
AWS X-Ray enables __________ to understand and optimize performance across microservices architectures.
- Administrators
- Database administrators
- Developers
- Network engineers
AWS X-Ray enables developers to understand and optimize performance across microservices architectures by providing insights into request flows, latency, and dependencies.
What are some best practices for using custom metrics in AWS?
- Define meaningful metrics
- Ignore anomalies
- Monitor regularly
- Use default alarms
Best practices for using custom metrics in AWS include defining meaningful metrics that align with business objectives, regularly monitoring metrics, and investigating anomalies rather than ignoring them.
Custom metrics in AWS are often collected using __________.
- Amazon CloudWatch
- Amazon EC2
- Amazon RDS
- Amazon S3
Custom metrics in AWS are often collected using Amazon CloudWatch.
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.