AWS Lambda provides __________ for building and deploying custom runtimes to simplify the process.
- Documentation
- SDKs
- Templates
- Tooling
AWS Lambda provides tooling for building and deploying custom runtimes, such as the AWS Lambda Runtime Interface Emulator and the AWS Lambda Runtime API, to simplify the process.
When using a custom runtime, you have full control over the __________ and execution environment of your Lambda functions.
- Infrastructure
- Networking
- Permissions
- Runtime
When using a custom runtime, you have full control over the runtime and execution environment of your Lambda functions.
Monitoring and debugging custom runtimes might require integration with AWS __________ services.
- CloudWatch
- Glacier
- RDS
- S3
Monitoring and debugging custom runtimes in AWS Lambda might require integration with AWS CloudWatch services.
What is Lambda@Edge?
- AWS service for running code closer to end-users
- Container service
- Database service
- Machine learning service
Lambda@Edge is an AWS service that allows you to run code closer to end-users, enabling you to customize content delivery and enhance user experience.
What is the primary purpose of Lambda@Edge?
- Analyzing data
- Customizing content delivery and enhancing user experience
- Managing databases
- Securing network traffic
The primary purpose of Lambda@Edge is to enable customization of content delivery and enhance user experience by running code closer to end-users.
AWS Lambda enforces a __________ for custom runtimes to ensure security and stability.
- Execution policy
- Hardware specification
- Runtime API
- Scripting language
AWS Lambda enforces a Runtime API for custom runtimes to ensure security and stability.
Scenario: Your team is developing a new programming language optimized for specific computational tasks. How would you integrate this language as a custom runtime in AWS Lambda?
- Create a Lambda layer with the language runtime
- Deploy the language runtime as a standalone service
- Embed the language runtime directly into the function code
- Use an existing natively supported runtime and adapt it
Creating a Lambda layer with the language runtime allows for reusability across multiple functions and simplifies maintenance.
Scenario: You need to ensure compliance with strict security requirements for your AWS Lambda functions, including custom runtime environments. How would you implement security controls and best practices?
- Enable AWS CloudTrail logging for Lambda function activity
- Encrypt environment variables containing sensitive data
- Implement least privilege IAM roles for Lambda functions
- Utilize AWS WAF to filter incoming requests
Implementing least privilege IAM roles for Lambda functions helps restrict access and reduce the attack surface, contributing to compliance with strict security requirements.
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.