What are the benefits of AWS Lambda providing support for custom runtimes?
- Enhanced security
- Improved performance
- Increased language support
- Reduced cost
AWS Lambda's support for custom runtimes allows developers to use programming languages and language versions that are not natively supported by AWS Lambda.
How does AWS Lambda handle updates and maintenance of supported runtimes?
- AWS manages updates and maintenance
- Developers are responsible for updates
- Runtimes are static and do not require updates
- Updates are managed by third-party vendors
AWS Lambda handles updates and maintenance of supported runtimes, ensuring that they are up-to-date and secure without requiring manual intervention from developers.
In what scenarios would you consider using a custom runtime for an AWS Lambda function?
- When you need to enhance security
- When you need to optimize performance
- When you need to reduce cost
- When you need to use a language or language version not supported by AWS Lambda
Custom runtimes are particularly useful when developers need to use programming languages or language versions that are not natively supported by AWS Lambda, providing flexibility for specific use cases.
AWS Lambda supports runtimes such as __________, __________, and __________.
- C++, Ruby, PHP
- Go, Rust, Swift
- PowerShell, TypeScript, Perl
- Python, Node.js, Java
AWS Lambda supports various runtimes including Python, Node.js, and Java, allowing developers to write functions in their preferred programming language.
The choice of runtime determines the __________ available for development and deployment of Lambda functions.
- Cloud provider
- Cost structure
- Libraries and frameworks
- Network bandwidth
The choice of runtime in AWS Lambda determines the libraries, frameworks, and language features available for development and deployment of Lambda functions.
AWS Lambda automatically handles runtime __________, freeing developers from infrastructure management tasks.
- Code optimization
- Database management
- Provisioning and scaling
- Security configuration
AWS Lambda automatically handles runtime provisioning and scaling, ensuring that resources are allocated as needed to handle incoming events.
Custom runtimes in AWS Lambda allow developers to use ________ languages not officially supported by AWS.
- Custom
- Non-standard
- Third-party
- Unconventional
Custom runtimes in AWS Lambda enable the use of non-standard languages, extending the platform's flexibility beyond officially supported ones.
Upgrading to a newer version of a runtime in AWS Lambda may introduce ________ and compatibility issues.
- Breakages
- Bugs
- Errors
- Incompatibilities
Upgrading to a newer runtime version in AWS Lambda may introduce breakages and compatibility issues, necessitating thorough testing before deployment.
Scenario: You need to develop a machine learning model using AWS Lambda. Which runtime option would you choose and why?
- Go runtime
- Java runtime
- Node.js runtime
- Python with TensorFlow runtime
Python with TensorFlow runtime is a suitable choice for developing machine learning models on AWS Lambda, as it provides the necessary libraries and frameworks for training and inference tasks.
Scenario: A project requires integration with a third-party library not supported by default AWS Lambda runtimes. How would you approach this challenge using custom runtimes?
- Create a custom runtime with the necessary dependencies
- Explore alternative AWS services for integration
- Rewrite the functionality without the third-party library
- Use AWS Lambda layers for library integration
Creating a custom runtime allows you to include the required third-party library dependencies, enabling integration with AWS Lambda.