How does Lambda@Edge impact the latency of content delivery?
- Increases latency by adding additional processing overhead
- Increases latency by routing requests through central servers
- No impact on latency
- Reduces latency by executing functions closer to the end-user
Lambda@Edge reduces latency by executing functions closer to the end-user, improving response times for content delivery.
What AWS services can trigger Lambda@Edge functions?
- AWS Lambda
- Amazon CloudFront
- Amazon RDS
- Amazon S3
Lambda@Edge functions can be triggered by events generated by Amazon CloudFront, allowing for dynamic content manipulation and delivery optimizations.
How does Lambda@Edge help improve content delivery performance?
- Executing code closer to viewers
- Increasing server capacity
- Managing networking hardware
- Optimizing database queries
Lambda@Edge allows code execution at CloudFront edge locations, reducing latency by executing code closer to viewers, thus improving content delivery performance.
What are some common use cases for Lambda@Edge?
- Batch processing
- Database management
- IoT device management
- Website personalization
Lambda@Edge allows for dynamic content customization based on viewer location, device type, or other factors, enhancing user experience.
How does Lambda@Edge integrate with AWS CloudFront?
- By allowing you to run custom code at CloudFront edge locations
- By managing database connections
- By optimizing container deployments
- By providing machine learning models
Lambda@Edge integrates with AWS CloudFront by enabling you to run custom code at edge locations, allowing for dynamic content customization and optimization.
Scenario: You are migrating an existing application to AWS Lambda but require a runtime environment that is not natively supported. How would you approach this using custom runtimes?
- Deploy the application on EC2 instances
- Develop a custom runtime using the AWS Lambda Runtime API
- Modify the application to use a natively supported runtime
- Utilize AWS Fargate for containerized execution
Developing a custom runtime using the AWS Lambda Runtime API allows for supporting the required runtime environment in AWS Lambda.
AWS Step Functions support __________, allowing you to run tasks concurrently and synchronize the results.
- Asynchronous execution
- Distributed computing
- Parallelism
- Sequential execution
AWS Step Functions support parallelism, allowing you to run tasks concurrently and synchronize the results, which can improve workflow efficiency.
AWS Step Functions can integrate with various AWS services including __________ for serverless orchestration.
- AWS Lambda
- Amazon EC2
- Amazon RDS
- Amazon S3
AWS Step Functions can integrate with AWS Lambda for serverless orchestration, allowing you to coordinate multiple AWS services as part of your workflows.
What are some recommended use cases for AWS Step Functions?
- IoT device management
- Orchestration of microservices
- Real-time analytics
- Web hosting
AWS Step Functions is recommended for orchestrating complex workflows involving multiple microservices, coordinating their execution, and handling error scenarios.
How does AWS Step Functions handle long-running tasks?
- Auto-retry mechanism
- Manual intervention
- State persistence
- Timeout termination
AWS Step Functions handles long-running tasks by maintaining the state of the workflow execution, allowing it to persist even if they take hours, days, or weeks to complete.
In AWS Step Functions, what is a state machine?
- A database table
- A physical server
- A visual representation of a workflow
- An encryption algorithm
In AWS Step Functions, a state machine is a visual representation of a workflow, defining the sequence of steps and transitions between them.
How does AWS Step Functions manage error handling and retries?
- Built-in error handling and automatic retries
- External error handling services
- Manual error handling only
- No error handling
AWS Step Functions provide built-in error handling capabilities, allowing you to define how to handle different types of errors and automatically retry failed steps based on configured retry policies.