Error handling in AWS Step Functions can be configured using __________, which define how the state machine reacts to errors.
- Catchers
- Handlers
- Resolvers
- Watchdogs
Catchers in AWS Step Functions define how the state machine reacts to errors, allowing you to specify recovery steps or handle exceptions gracefully.
The __________ feature of AWS Step Functions enables you to trigger actions based on the success or failure of a state machine execution.
- Error handling
- Event-driven architecture
- Load balancing
- Retry mechanism
Error handling in AWS Step Functions allows you to define actions to take based on the success or failure of specific states within a state machine execution.
AWS Step Functions can be used to coordinate __________ workflows that involve multiple AWS services and custom business logic.
- Automation
- Load balancing
- Orchestration
- Virtualization
AWS Step Functions provide orchestration capabilities, allowing you to coordinate workflows involving multiple AWS services and custom business logic, thereby automating complex tasks.
Scenario: You are designing a data processing workflow that involves multiple AWS services such as S3, Lambda, and DynamoDB. Which AWS service would you use to orchestrate the workflow and handle error handling?
- AWS ECS
- AWS Glue
- AWS Step Functions
- Amazon SQS
AWS Step Functions provides a serverless orchestration service that allows you to coordinate multiple AWS services, including error handling and retry logic, in a visual workflow.
What is the Serverless Framework used for?
- Building and deploying serverless applications
- Managing containers
- Managing databases
- Managing virtual machines
The Serverless Framework is used for building and deploying serverless applications across multiple cloud providers, including AWS, Azure, and Google Cloud Platform.
How do AWS SAM and the Serverless Framework differ in their approach to deploying serverless applications?
- AWS SAM and the Serverless Framework require manual deployment
- AWS SAM and the Serverless Framework use identical deployment approaches
- AWS SAM uses CloudFormation for deployment
- The Serverless Framework uses its own deployment mechanism
AWS SAM uses CloudFormation for deployment, while the Serverless Framework uses its own deployment mechanism, abstracting away the underlying infrastructure.
What are the key features of AWS SAM?
- Advanced machine learning capabilities
- Built-in monitoring tools
- Hybrid cloud support
- Simplified syntax and predefined templates
AWS SAM provides a simplified syntax for defining serverless applications and includes predefined templates for common use cases, reducing development time and complexity.
In what programming languages can you define AWS SAM templates and Serverless Framework configurations?
- Bash scripting
- Java and C#
- Python only
- YAML and JSON
Both AWS SAM templates and Serverless Framework configurations can be defined using YAML or JSON, providing flexibility and ease of use for developers familiar with these formats.
How does AWS SAM integrate with AWS CloudFormation?
- AWS SAM bypasses CloudFormation
- AWS SAM creates separate stacks from CloudFormation
- AWS SAM is a standalone service
- AWS SAM templates are an extension of CloudFormation
AWS SAM (Serverless Application Model) integrates with AWS CloudFormation by using SAM templates, which are an extension of CloudFormation templates and offer simplified syntax for defining serverless applications.
What are some advanced features offered by the Serverless Framework?
- Auto-scaling
- Built-in monitoring
- Multi-region deployment
- Rollback support
The Serverless Framework provides advanced features such as built-in monitoring, which allows you to track the performance and health of your serverless applications.