How does a custom runtime differ from standard runtimes in AWS Lambda?
- Custom runtimes are only available for enterprise-tier AWS Lambda plans, while standard runtimes are included in all tiers.
- Custom runtimes enable the use of programming languages not officially supported by AWS Lambda, while standard runtimes are limited to a predefined set of languages.
- Custom runtimes offer greater control over the execution environment and configuration options compared to standard runtimes.
- Custom runtimes provide better performance and scalability compared to standard runtimes in AWS Lambda.
Custom runtimes enable the use of programming languages not officially supported by AWS Lambda, expanding the range of languages developers can use.
What is the primary advantage of using a custom runtime in AWS Lambda?
- Custom runtimes offer better performance and resource optimization compared to standard runtimes in AWS Lambda.
- Custom runtimes provide built-in monitoring and debugging tools for AWS Lambda functions, streamlining development and troubleshooting processes.
- The primary advantage of using a custom runtime is the ability to leverage existing libraries and frameworks in languages not officially supported by AWS Lambda.
- Using a custom runtime in AWS Lambda eliminates the need for managing server infrastructure, reducing operational overhead and costs.
The primary advantage of using a custom runtime is the ability to leverage existing libraries and frameworks in languages not officially supported by AWS Lambda.
How can you create a custom runtime for AWS Lambda?
- Modifying the AWS Lambda source code
- Uploading a custom Docker image
- Using a third-party service
- Using the AWS Lambda Runtime API
Using the AWS Lambda Runtime API allows you to build custom runtimes by implementing a specific interface for handling function invocations, initialization, and cleanup.
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.
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.
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.