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.
Strategies such as __________ can help mitigate issues related to cold starts and concurrent execution spikes.
- Auto scaling
- Elastic load balancing
- Provisioned concurrency
- Static scaling
Provisioned concurrency allows you to preallocate resources to a function, reducing cold starts and mitigating issues related to concurrent execution spikes in AWS Lambda.
When architecting for high concurrency, it's crucial to design for __________ to ensure efficient resource utilization.
- Microservices architecture
- Monolithic architecture
- Stateful functions
- Stateless functions
Designing functions to be stateless allows them to scale horizontally and efficiently handle high concurrency in AWS Lambda, ensuring optimal resource utilization.
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.
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.
What are some considerations to keep in mind when using Lambda Layers in AWS Lambda?
- Hardware specifications
- Memory allocation
- Network latency
- Versioning and permissions
When using Lambda Layers in AWS Lambda, considerations such as versioning and permissions are important to manage updates and control access to the layers.