What strategies can be employed to optimize concurrency and scaling in AWS Lambda?
- Horizontal scaling
- Manual scaling
- Provisioning concurrency
- Vertical scaling
Provisioning concurrency allows you to allocate a set number of execution environments, ensuring consistent performance and reducing cold start times in AWS Lambda.
What are some limitations to consider when designing highly concurrent AWS Lambda applications?
- Account-level concurrency limits
- Cold start latency
- Event source limits
- Resource contention
AWS Lambda imposes account-level concurrency limits, which can restrict the maximum number of concurrent executions across all functions in the account, requiring careful planning and monitoring.
AWS Lambda automatically handles __________, allowing multiple instances of a function to run concurrently.
- Authentication
- Containerization
- Load balancing
- Scaling
AWS Lambda automatically handles scaling, allowing multiple instances of a function to run concurrently.
When designing AWS Lambda functions for high concurrency, it's essential to consider the impact on __________ and resource consumption.
- Cost
- Latency
- Performance
- Security
When designing AWS Lambda functions for high concurrency, it's essential to consider the impact on performance and resource consumption.
AWS Lambda provides __________ concurrency limits per region by default.
- Account-based
- Function-based
- Global
- Region-based
AWS Lambda provides account-based concurrency limits per region by default.
To control concurrency in AWS Lambda, you can set __________ at the function level.
- Execution role
- Memory allocation
- Reserved concurrency
- Timeout duration
Reserved concurrency allows you to limit the number of concurrent executions of a function, helping you control costs and resource utilization in AWS Lambda.
How do Lambda Layers simplify code management in AWS Lambda?
- By allowing shared code and dependencies across multiple functions
- By automating deployment processes
- By optimizing runtime performance
- By restricting access to functions
Lambda Layers simplify code management in AWS Lambda by allowing you to package common code and dependencies separately from your function code, making it easier to update and maintain shared components.
In AWS Lambda, how are Lambda Layers applied to a function?
- By attaching them to a function's configuration
- By configuring networking settings
- By creating separate Lambda functions
- By embedding them in function code
Lambda Layers are applied to a function in AWS Lambda by attaching them to the function's configuration, either via the AWS Management Console, AWS CLI, or AWS SDKs, allowing the function to access the shared code and dependencies during execution.
What are Lambda Layers used for?
- Encrypting data
- Managing database connections
- Sharing code and dependencies across multiple functions
- Storing function logs
Lambda Layers are used in AWS Lambda to share common code, libraries, and dependencies across multiple functions, reducing duplication and improving maintainability.
How can Lambda Layers help in sharing code across multiple Lambda functions?
- By automatically synchronizing code
- By embedding code directly into functions
- By limiting access to code
- By providing a common set of libraries and dependencies
Lambda Layers help in sharing code across multiple Lambda functions by providing a common set of libraries and dependencies that can be reused.