Serverless computing encourages a __________ approach to development, promoting small, focused functions.
- Distributed
- Microservices
- Modular
- Monolithic
Serverless computing promotes a microservices architecture, where applications are composed of small, independent functions that each perform a specific task.
Scenario: Your company is considering migrating its existing applications to a serverless architecture. What factors would you consider during the migration planning phase?
- Application architecture, performance requirements, and cost optimization
- Data center location
- Hardware specifications
- Network bandwidth
Factors such as application architecture, performance requirements, and cost optimization should be considered during the planning phase of migrating existing applications to a serverless architecture.
Scenario: You are experiencing unexpected spikes in traffic to your serverless application, causing performance issues. How would you address this scalability challenge?
- Configure auto-scaling policies for AWS Lambda
- Increase instance size for Amazon EC2
- Manually add more servers
- Optimize database queries
Configuring auto-scaling policies for AWS Lambda allows it to automatically scale up or down based on incoming traffic, making it a suitable solution for addressing unexpected spikes in traffic in a serverless application.
What is the core concept behind AWS Lambda's execution model?
- Batch processing
- Event-driven computing
- Predictive analytics
- Real-time processing
AWS Lambda's execution model is event-driven, meaning it executes functions in response to events such as changes to data or system state.
Which of the following describes how AWS Lambda manages server resources?
- Automatically scales
- Limits resource usage
- Manually allocates resources
- Requires constant monitoring
AWS Lambda automatically scales resources to handle incoming requests, ensuring optimal performance without manual intervention.
AWS Lambda manages the execution environment, including __________ and __________.
- Deployment and monitoring
- Infrastructure and scaling
- Logging and authentication
- Networking and security
AWS Lambda manages the underlying infrastructure and handles automatic scaling based on the incoming request traffic.
The duration of a cold start in AWS Lambda depends on factors such as __________ and __________.
- AWS region and service integration
- CloudWatch logs and event triggers
- Function size and language runtime
- Network speed and memory allocation
The size of the function package and the chosen language runtime affect the duration of a cold start in AWS Lambda.
AWS Lambda function execution can be optimized through __________ and __________ adjustments.
- Billing options and service quotas
- Language runtime and AWS region
- Memory allocation and timeout
- Network configuration and security settings
Optimizing memory allocation and adjusting timeout settings can improve the performance and efficiency of AWS Lambda functions.
AWS Lambda allocates resources dynamically based on __________ and __________.
- Data size, memory requirements
- Incoming request rate, configured concurrency limits
- Instance types, availability zones
- Time of day, network bandwidth
AWS Lambda dynamically allocates resources based on the incoming request rate and the configured concurrency limits. This allows it to scale automatically to handle varying workloads.
The execution model of AWS Lambda ensures __________ and __________ for functions.
- Fixed resource allocation, high latency
- Manual intervention, resource constraints
- Predictable execution time, low throughput
- Scalability, fault tolerance
AWS Lambda's execution model ensures scalability by automatically scaling resources based on demand and fault tolerance by handling failures transparently.