What are custom metrics used for in AWS?
- Configuring IAM policies
- Monitoring network traffic
- Monitoring server hardware metrics
- Monitoring specific application or business metrics
Custom metrics in AWS are used for monitoring specific application or business metrics that are not available by default through AWS services.
How are custom metrics typically created in AWS?
- Automatic discovery by CloudWatch
- Manual configuration through the AWS Management Console
- Using AWS Lambda functions
- Using the CloudWatch API
Custom metrics in AWS are typically created using the CloudWatch API, allowing developers to programmatically send data to CloudWatch for monitoring.
What is the primary benefit of using custom metrics in AWS monitoring?
- Managing IAM users
- Monitoring AWS service health
- Monitoring application-specific performance
- Monitoring infrastructure uptime
The primary benefit of using custom metrics in AWS monitoring is the ability to monitor application-specific performance metrics that are crucial for your business or application.
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.
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.
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.