Scenario: Your application's performance is degrading, and you suspect it's due to excessive logging. How would you use CloudWatch Logs to identify and mitigate this issue?
- Disable logging altogether
- Increase logging verbosity
- Manually review log files
- Set up log metric filters and alarms
By setting up log metric filters and alarms in CloudWatch Logs to extract specific patterns from log events related to performance degradation and alerting when these metrics exceed thresholds, you can identify and mitigate issues caused by excessive logging.
Scenario: You need to comply with regulatory requirements to retain log data for seven years. How would you configure CloudWatch Logs to meet this requirement effectively?
- Create retention policies
- Increase log group size
- Manually delete old log data
- Use CloudTrail instead
By creating retention policies in CloudWatch Logs, you can specify the retention period for log data, ensuring that it is retained for the required duration of seven years to comply with regulatory requirements.
How does AWS X-Ray help in understanding application performance?
- Generates synthetic traffic
- Manages server resources
- Optimizes network bandwidth
- Provides insights into latency and errors
AWS X-Ray provides insights into latency and errors by tracing requests and capturing data such as response times and error rates, helping you identify performance bottlenecks.
In what way does AWS X-Ray provide insights into distributed applications?
- Encrypts data in transit
- Manages server instances
- Performs load testing
- Visualizes request flow
AWS X-Ray visualizes the flow of requests through distributed applications, showing how requests are processed and which components are involved, aiding in understanding application architecture and performance.
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.
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.