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.
Scenario: You are tasked with optimizing the performance of a microservices-based application. How would you use AWS X-Ray to identify and address performance issues?
- Use X-Ray to manage database connections
- Use X-Ray to monitor server CPU utilization
- Use X-Ray to provision additional resources
- Use X-Ray traces to analyze the flow of requests between microservices
Using X-Ray traces, you can analyze the flow of requests between microservices to identify and address performance issues in a microservices-based application.
Scenario: Your team is deploying a new feature that involves multiple AWS services. How can AWS X-Ray help in ensuring the smooth integration and performance of these services?
- Use X-Ray to deploy the feature automatically
- Use X-Ray to manage user authentication
- Use X-Ray to provision additional resources
- Use X-Ray to trace requests across different AWS services
AWS X-Ray can be used to trace requests across different AWS services, ensuring smooth integration and identifying any performance issues or errors during the deployment of a new feature involving multiple services.
Scenario: A critical production application is experiencing intermittent slowdowns. How would you leverage AWS X-Ray to troubleshoot and resolve these performance issues?
- Analyze X-Ray traces to identify latency and errors in service calls
- Use X-Ray to manage DNS settings
- Use X-Ray to restart application instances
- Use X-Ray to schedule maintenance tasks
Leveraging AWS X-Ray, you can analyze traces to identify latency and errors in service calls, helping troubleshoot and resolve intermittent slowdowns in a critical production application.
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 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 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.
What is the significance of CloudWatch Logs in relation to AWS Lambda error handling?
- Manages function concurrency
- Provides security metrics
- Stores log data for monitoring
- Triggers Lambda functions
CloudWatch Logs store log data from AWS Lambda functions, which is crucial for monitoring and troubleshooting errors.