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.
Which of the following programming languages is NOT supported as a runtime for AWS Lambda?
- COBOL
- Java
- Python
- Ruby
COBOL is not supported as a runtime for AWS Lambda. AWS Lambda primarily supports modern programming languages like Python, Node.js, Java, and others.
The flexibility of custom runtimes enables fine-tuning of ________ and dependencies for specific use cases.
- Performance
- Resource usage
- Scalability
- Security
Custom runtimes in AWS Lambda allow for fine-tuning of performance-related parameters and dependencies, optimizing functions for specific use cases.
Scenario: Your team is considering migrating existing applications to AWS Lambda. How would you evaluate the compatibility of the current runtimes with AWS Lambda?
- Conduct compatibility tests with existing codebase
- Consult AWS Lambda experts
- Review AWS Lambda documentation and runtime support
- Trial migration with a sample application
Reviewing AWS Lambda documentation and runtime support is crucial to understanding which runtimes are officially supported and compatible with AWS Lambda.