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 are the limitations of Lambda@Edge compared to regular AWS Lambda functions?
- Access to fewer AWS services
- Higher memory allocation
- Longer maximum execution time
- Smaller function size limit
Lambda@Edge functions have a smaller size limit compared to regular AWS Lambda functions due to the constraints of edge computing environments.
Can Lambda@Edge functions access resources in a VPC?
- Limited access, requiring special permissions
- No, Lambda@Edge functions cannot access resources in a VPC
- Partial access, depending on VPC configuration
- Yes, Lambda@Edge functions have full access to resources in a VPC
Lambda@Edge functions execute at edge locations and do not have access to resources within a VPC due to the distributed nature of edge computing.
Lambda@Edge enables you to customize content delivery based on the viewer's __________.
- Browser
- Location
- Operating system
- Time zone
Lambda@Edge enables you to customize content delivery based on the viewer's geographic location, enabling personalized experiences.
The deployment of Lambda@Edge functions is managed through AWS __________.
- CloudFront
- Elastic Beanstalk
- IAM
- Route 53
The deployment of Lambda@Edge functions is managed through AWS CloudFront, which integrates with Lambda@Edge to execute functions at edge locations.
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.
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.
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.
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 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.