AWS Step Functions is a powerful service for orchestrating complex workflows in the AWS ecosystem. If you’re preparing for an AWS interview or just want to deepen your knowledge, here are some basic as well as advanced AWS Step Functions interview questions and their answers.
Top 10 AWS Step Functions Interview Questions
1. What are AWS Step Functions, and how do they fit within the AWS serverless ecosystem?
Answer: AWS Step Functions is a serverless workflow service that allows you to coordinate and manage the execution of multiple AWS services, such as AWS Lambda and AWS Fargate. It provides an easy-to-use interface to create, visualize, and manage workflows, which are composed of a series of steps, with each step representing an individual task or function. AWS Step Functions fits into the serverless ecosystem by orchestrating and coordinating the execution of various AWS services, reducing the need for custom application code to manage workflows.
2. Can you explain the difference between AWS Step Functions Standard and Express Workflows? When should you use each?
Answer: AWS Step Functions offers two workflow types: Standard Workflows and Express Workflows.
- Standard Workflows are designed for long-running and complex workflows, with an execution duration of up to one year. They provide features such as error handling, retries, and various state types for more complex orchestration scenarios.
- Express Workflows are designed for high-volume, short-running workflows, with an execution duration of up to five minutes. They are optimized for cost and performance and are ideal for use cases like data processing or streaming applications.
You should choose Standard Workflows for long-running, complex workflows that require advanced features like error handling and retries. Use Express Workflows when you need to process a large number of short-lived, high-volume workflows with low latency and cost.
Related Reading: A Comprehensive Comparison of Standard vs Express Workflows in AWS Step Functions
3. What are the key components of a Step Functions state machine definition, and what purpose does each serve?
Answer: A state machine definition in AWS Step Functions consists of several key components:
- States: The individual steps or tasks in the workflow, which can be of different types like Task, Choice, Wait, Fail, Succeed, or Parallel.
- Transitions: The connections between states that define the flow of the workflow. Transitions can be based on the output of a previous state or conditional branching.
- StartAt: Specifies the first state to be executed in the workflow.
- TimeoutSeconds: An optional parameter that sets the maximum duration of a state execution.
- Retry: An optional configuration that specifies how to handle retries when a state fails.
- Catch: An optional configuration that specifies how to handle errors when a state fails.
These components work together to define the workflow’s execution flow, error handling, and retry logic.
4. What is the significance of the “StartAt” attribute in a Step Functions state machine definition?
Answer: The “StartAt” attribute in a Step Functions state machine definition is used to specify the initial state to be executed when the state machine starts. It is a mandatory attribute that defines the entry point of the workflow. The state specified by the “StartAt” attribute must exist within the state machine definition.
5. How can you use AWS Step Functions with AWS Lambda? Explain the process of invoking a Lambda function from a Step Function state.
Answer: AWS Step Functions can be used with AWS Lambda to orchestrate Lambda functions as part of a serverless workflow. To invoke a Lambda function from a Step Functions state, you need to define a Task state with the “Resource” field set to the Amazon Resource Name (ARN) of the Lambda function.
Here’s an example in JSON format:
{ "StartAt": "InvokeLambda", "States": { "InvokeLambda": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:FUNCTION_NAME", "End": true } } }
In this example, the InvokeLambda
state is a Task state that calls the specified Lambda function using its ARN. When the state machine executes, it starts with the InvokeLambda
state, which in turn invokes the Lambda function. Once the Lambda function completes its execution, the state machine execution ends since the End
attribute is set to true
.
6. What are the different types of states available in AWS Step Functions? Provide a brief description of each.
Answer: AWS Step Functions offers several types of states to define various tasks and control flow within a state machine:
- Task: Represents a single unit of work, such as invoking a Lambda function, running an AWS Fargate task, or interacting with another AWS service. Task states are the primary building blocks of Step Functions workflows.
- Choice: Provides conditional branching based on the output of a previous state. A Choice state can have multiple branches, each with a condition and a target state.
- Wait: Introduces a delay before transitioning to the next state. Wait states can be configured with a fixed duration or a timestamp to wait until.
- Succeed: Indicates that the state machine has successfully completed its execution. A Succeed state immediately terminates the execution and returns a successful result.
- Fail: Indicates that the state machine has encountered an error and must terminate its execution. A Fail state immediately terminates the execution and returns an error result.
- Parallel: Executes multiple branches concurrently and waits for all branches to complete before transitioning to the next state. Parallel states are useful for improving performance by executing independent tasks simultaneously.
These state types can be combined in various ways to create complex workflows with conditional branching, error handling, retries, and parallelism.
7. How are AWS Step Functions billed? Explain the pricing model for both Standard and Express Workflows.
Answer: AWS Step Functions uses a pay-as-you-go pricing model with different billing parameters for Standard Workflows and Express Workflows.
- Standard Workflows: Billed based on the number of state transitions and the total duration of the workflow executions. Each state transition (entering or exiting a state) is charged, and the total duration is rounded up to the nearest 100 milliseconds.
- Express Workflows: Billed based on the number of state transitions and the total duration of the workflow executions, similar to Standard Workflows. However, Express Workflows have a lower cost per state transition and duration, making them more cost-effective for high-volume, short-running workflows.
In addition to these charges, you may also incur costs for the AWS services used within your Step Functions workflows, such as AWS Lambda, Amazon S3, or AWS Fargate.
Realted Reading: Read our comprehensive guide on controlling your Step Functions cost and mastering its pricing.
8. What are the execution limits for AWS Step Functions, and how can they impact the design of your workflows?
Answer: AWS Step Functions has several execution limits that you should be aware of when designing your workflows:
- Execution Duration: The maximum duration for a single execution is one year for Standard Workflows and five minutes for Express Workflows.
- State Machine Definition Size: The maximum size of a state machine definition is 256 KB.
- Input and Output Payload Size: The maximum payload size for inputs and outputs of a state is 256 KB.
- Maximum Number of States: A single state machine can have up to 25,000 states.
- Retry and Catch Limits: A maximum of three retries and three catch configurations can be defined per state.
These limits can impact the design of your workflows by necessitating the use of parallel states, partitioning large workflows into smaller ones, or optimizing the size of input and output payloads.
9. How can you use AWS Step Functions to implement human approval workflows? Briefly explain the process.
Answer: Implement human approval workflows with AWS Step Functions using Task states, Task tokens, and AWS services:
- Create Task state with Task token: Add a Task state generating a Task token in the state machine definition.
- Send Task token to approver: Use Amazon SNS or Amazon SES to send the Task token to the approver, either via service integration or invoking a Lambda function.
- Approver takes action: Approver reviews the task, makes a decision, and sends a response to the Step Functions API with the Task token and decision.
- Resume workflow: The Step Functions API resumes the paused workflow based on the approval or rejection decision and continues along different branches defined by Choice states.
This approach enables human approval workflows that automatically pause and resume based on human input.
10. How do AWS Step Functions integrate with other AWS services like Amazon S3, DynamoDB, and API Gateway?
Answer: AWS Step Functions enables seamless integration with various AWS services using methods like service integration, AWS SDKs, event triggers, and API Gateway integration.
- Service Integration: Step Functions provides native service integration for many AWS services, including Lambda, AWS Fargate, Amazon SNS, Amazon SQS, and more. This allows you to directly interact with these services within your Step Functions workflow by defining Task states that call the specific service APIs.
- AWS SDKs: You can use the AWS SDKs in your Lambda functions or other compute resources to interact with AWS services like Amazon S3, Amazon DynamoDB, or Amazon API Gateway. This approach allows you to create custom actions and interactions between Step Functions and the AWS services as needed.
- Event Triggers: You can use Amazon EventBridge or CloudWatch Events to trigger a Step Functions state machine execution based on events from other AWS services. For example, you can start a workflow when a new object is uploaded to an Amazon S3 bucket or when a specific Amazon CloudWatch alarm is triggered.
- API Gateway Integration: You can create an API Gateway endpoint that triggers a Step Functions state machine execution. This allows you to start workflows using HTTP requests and integrate your Step Functions workflows into web applications or other systems that communicate over HTTP.
These integrations enable you to create powerful, serverless workflows that orchestrate various AWS services to accomplish complex tasks and automate business processes.
11. How can you monitor and log AWS Step Functions performance and execution details?
Answer: AWS Step Functions enables you to effectively monitor and log workflow performance and execution details through various features as described below:
- AWS Management Console: Visualize state machine execution history, state flow, and execution details in the console.
- CloudWatch Metrics: Automatically sent metrics enable performance monitoring and health checks.
- CloudWatch Logs: Enable logging to collect detailed state transition information, including errors.
- AWS X-Ray: Activate X-Ray tracing for performance insights and workflow behavior analysis.
- Execution Event History: Access state transition details, errors, and input/output data through the event history.
- Step Functions API: Programmatically monitor execution status and retrieve execution history.
- Lambda Function Logging: Troubleshoot Lambda functions and analyze performance using logs in CloudWatch Logs.
- Third-Party Monitoring Tools: Integrate external monitoring tools using AWS SDKs, API operations, or by exporting metrics and logs.
These capabilities provide visibility, troubleshooting, and optimization opportunities for your workflows.
12. How do you secure AWS Step Functions state machines and executions?
Answer: To secure AWS Step Functions, using service and features such as IAM, resource policies, encryption, execution isolation, VPC endpoints, and auditing :
- IAM: Use IAM policies to control access to state machines, Step Functions API, and execution history.
- Resource Policies: Attach resource policies for specific access control, including cross-account and intra-organization access.
- Encryption: Use AWS KMS to encrypt state machine definitions, execution data, and logs at rest.
- Execution Isolation: Control access to AWS resources like Lambda functions or S3 buckets through IAM and resource policies.
- VPC Endpoints: Create VPC endpoints to keep execution traffic within your Amazon VPC for enhanced security.
- Auditing and Compliance: Integrate with AWS CloudTrail for auditing API calls and resource modifications.
These security features can be leveraged to ensure that the state machines and executions are secure and compliant with your organization’s requirements.
Related Reading: AWS Security Best Practices
13. What are strategies to reduce AWS Step Functions costs, focusing on state transitions, execution duration, and Standard vs. Express Workflows?
Answer: To reduce AWS Step Functions costs, consider the following strategies:
- Optimize State Transitions: Reduce state transitions by simplifying workflow logic or combining states when possible.
- Minimize Execution Duration: Optimize components your state machine interacts with, such as Lambda functions and external services, to reduce duration.
- Choose Appropriate Workflow Type: Evaluate your use case and select the workflow type (Standard or Express) that offers the lowest cost based on your workload and execution patterns.
- Use Service Integrations: Simplify connections to other AWS services with service integrations, reducing state transitions and workflow complexity.
- Set Timeouts and Retries: Configure appropriate timeout values and retry policies to avoid unnecessary charges and excessive retries.
- Monitor and Analyze Usage: Regularly monitor and analyze your Step Functions usage and costs to identify patterns and optimize expenses.
Implementing these strategies helps reduce costs while maintaining efficient workflows.
AWS Step Functions Advanced Questions
1. How do you define parallel tasks in a Step Functions workflow? Please provide a code snippet in JSON or YAML format.
Answer: To define parallel tasks in an AWS Step Functions workflow, you can use the Parallel
state. The Parallel
state allows you to execute multiple branches concurrently. Each branch is a separate, complete state machine definition. Here’s an example in JSON format:
{ "StartAt": "ParallelState", "States": { "ParallelState": { "Type": "Parallel", "Branches": [ { "StartAt": "Task1", "States": { "Task1": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:Task1Function", "End": true } } }, { "StartAt": "Task2", "States": { "Task2": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:Task2Function", "End": true } } } ], "Next": "FinalState" }, "FinalState": { "Type": "Pass", "Result": "Parallel execution completed", "End": true } } }
In this example, the Parallel
state “ParallelState” has two branches, each with one task. Task1 and Task2 execute concurrently. Once both branches complete their execution, the workflow moves to the “FinalState” state.
2. How can we perform error handling and retries in AWS Step Functions? Can you explain the various retry strategies?
Answer: AWS Step Functions provides error handling and retry capabilities to manage failures in your workflows. You can define error handling and retry strategies at the state level.
Error Handling: You can specify a Catch
field in a state definition to catch specific errors and move the execution to a different state. For example:
{ "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:ExampleFunction", "Catch": [ { "ErrorEquals": ["States.TaskFailed"], "Next": "HandleTaskFailedError" } ] }
In this example, if the ExampleFunction
task encounters a States.TaskFailed
error, the execution will move to the “HandleTaskFailedError” state.
Retry Strategies: You can specify a Retry
field in a state definition to configure a retry strategy. A retry strategy can include the following parameters:
ErrorEquals
: A list of error names for which the retry policy applies.IntervalSeconds
: The wait time between retry attempts, in seconds.MaxAttempts
: The maximum number of retry attempts.BackoffRate
: The rate at which the wait time between retries increases (exponential backoff).
For example:
{ "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:ExampleFunction", "Retry": [ { "ErrorEquals": ["States.TaskFailed"], "IntervalSeconds": 2, "MaxAttempts": 3, "BackoffRate": 2.0 } ] }
In this example, if the ExampleFunction
task encounters a States.TaskFailed
error, it will retry up to 3 times with an initial wait time of 2 seconds between attempts and an exponential backoff rate of 2.0.
3. How do you pass data between states in an AWS Step Functions workflow? Provide a code snippet in JSON or YAML format.
Answer: You can pass data between states in an AWS Step Functions workflow using the InputPath
, OutputPath
, and ResultPath
fields in the state definition.
InputPath
: Filters the input data to the state.OutputPath
: Filters the output data from the state.ResultPath
: Specifies the JSON path to store the output data from the state.
Here’s an example in JSON format:
{ "StartAt": "Task1", "States": { "Task1": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:Task1Function", "InputPath": "$.inputData", "ResultPath": "$.task1Output", "Next": "Task2" }, "Task2": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:Task2Function", "InputPath": "$.task1Output", "ResultPath": "$.task2Output", "End": true } } }
In this example, the Task1
state takes its input from the $.inputData
path of the input JSON, and its output is stored in the $.task1Output
path. The Task2
state takes its input from the $.task1Output
path and stores its output in the $.task2Output
path.
4. How can you use AWS Step Functions to implement a serverless workflow with AWS Fargate? Provide a brief explanation and code snippet in JSON or YAML format.
Answer: You can use AWS Step Functions to implement a serverless workflow with AWS Fargate by invoking an AWS Fargate task as part of your state machine. You can use the ECS
service integration to run the Fargate task. Here’s an example in JSON format:
{ "StartAt": "RunFargateTask", "States": { "RunFargateTask": { "Type": "Task", "Resource": "arn:aws:states:::ecs:runTask.sync", "Parameters": { "LaunchType": "FARGATE", "PlatformVersion": "LATEST", "TaskDefinition": "arn:aws:ecs:REGION:ACCOUNT_ID:task-definition/TaskDefinitionName:Version", "Cluster": "arn:aws:ecs:REGION:ACCOUNT_ID:cluster/ClusterName", "NetworkConfiguration": { "AwsvpcConfiguration": { "Subnets": ["subnet-abc123"], "AssignPublicIp": "ENABLED" } } }, "End": true } } }
In this example, the “RunFargateTask” state uses the ECS
service integration to run an AWS Fargate task. The TaskDefinition
, Cluster
, and NetworkConfiguration
parameters are used to configure the Fargate task.
5. Explain some of the AWS Step Functions best practices to optimize workflow performance and cost?
Answer: Implement these best practices to optimize AWS Step Functions workflow performance and cost:
- Select the right workflow type: Choose between Standard and Express Workflows based on use case, considering duration and volume.
- Reduce state transitions: Minimize state transitions to improve performance and lower costs.
- Optimize Lambda functions: Balance cost and performance by tuning Lambda function settings.
- Filter data with InputPath and OutputPath: Streamline data flow between states, improving performance and avoiding data size limits.
- Use ResultPath for intermediate results: Store intermediate results, keeping original input data and relevant results only.
- Handle errors and retries: Implement error handling, using exponential backoff for retries to avoid throttling.
- Increase execution concurrency: For large event volumes, use parallelism to boost performance.
- Leverage EventBridge for event-driven workflows: Decouple event producers and consumers for better scalability and flexibility.
- Monitor and log workflows: Utilize Amazon CloudWatch for monitoring and logging, setting alarms for issues.
- Regularly review and optimize workflow: Periodically assess workflow, using visualizations to identify bottlenecks or optimizations.
Adopting these best practices enhances performance, cost-effectiveness, reliability, and scalability in AWS Step Functions workflows.
6. How do you set up dynamic parallelism in AWS Step Functions? Provide a code snippet in JSON or YAML format.
Answer: Dynamic parallelism in AWS Step Functions enables you to process multiple items concurrently within a single state machine execution. You can use the Map
state to process an array of items in parallel.
Here’s an example in JSON format:
{ "StartAt": "DynamicParallelism", "States": { "DynamicParallelism": { "Type": "Map", "ItemsPath": "$.items", "Iterator": { "StartAt": "ProcessItem", "States": { "ProcessItem": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:ProcessItemFunction", "End": true } } }, "End": true } } }
In this example, the DynamicParallelism
state is a Map
state that processes the items in the input JSON’s $.items
array in parallel. The Iterator
field defines the state machine to execute for each item. In this case, the ProcessItem
state invokes a Lambda function to process each item.
7. Provide a code snippet that demonstrates how to set up a CloudWatch Event to trigger an AWS Step Functions state machine execution.
Answer: To set up a CloudWatch Event to trigger an AWS Step Functions state machine execution, you need to create an event rule and set the state machine as the target. Here’s an example using AWS CLI:
aws events put-rule --name "StepFunctionTriggerRule" --schedule-expression "rate(1 hour)" aws events put-targets --rule "StepFunctionTriggerRule" --targets "Id"="StepFunctionTarget","Arn"="arn:aws:states:REGION:ACCOUNT_ID:stateMachine:StateMachineName"
In this example, the first command creates a CloudWatch Event rule named “StepFunctionTriggerRule” that runs every hour. The second command adds a target to the rule, specifying the state machine ARN as the target.
8. How do you enable AWS X-Ray tracing for an AWS Step Functions state machine? Provide a code snippet in JSON or YAML format.
Answer: To enable AWS X-Ray tracing for an AWS Step Functions state machine, you need to add the TracingConfiguration
field to the state machine definition. Here’s an example in JSON format:
{ "StartAt": "ExampleState", "States": { "ExampleState": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:ExampleFunction", "TracingConfiguration": { "Enabled": true }, "End": true } } }
In this example, the ExampleState
has the TracingConfiguration
field with the Enabled
property set to true
. This enables X-Ray tracing for the Lambda function invoked by this state.
9. Demonstrate how to define an AWS Step Functions Choice state with a code snippet in JSON or YAML format.
Answer: The Choice
state in AWS Step Functions allows you to make decisions based on the input data and transition to different states. Here’s an example in JSON format:
{ "StartAt": "ChoiceState", "States": { "ChoiceState": { "Type": "Choice", "Choices": [ { "Variable": "$.inputValue", "NumericEquals": 1, "Next": "StateA" }, { "Variable": "$.inputValue", "NumericEquals": 2, "Next": "StateB" } ], "Default": "DefaultState" }, "StateA": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:FunctionA", "End": true }, "StateB": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:FunctionB", "End": true }, "DefaultState": { "Type": "Fail", "Error": "DefaultStateError", "Cause": "No matching choice was found." } } }
In this example, the ChoiceState
evaluates the value of $.inputValue
in the input data. Based on the value, it transitions to either StateA
or StateB
. If no matching choice is found, it transitions to the DefaultState
, which is a Fail
state.
10. Please provide a code snippet to demonstrate how to implement a custom retry policy for a specific task in a Step Functions workflow.
Answer: In AWS Step Functions, you can implement a custom retry policy for a specific task by using the Retry
field. Here’s an example in JSON format:
{ "StartAt": "ExampleTask", "States": { "ExampleTask": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:ExampleFunction", "Retry": [ { "ErrorEquals": ["CustomError"], "IntervalSeconds": 2, "MaxAttempts": 3, "BackoffRate": 2.0 } ], "End": true } } }
In this example, the ExampleTask
state defines a custom retry policy for the CustomError
error. The policy specifies an initial interval of 2 seconds, a maximum of 3 attempts, and a backoff rate of 2.0.
11. What are AWS Step Functions dynamic parallelism, and how can they be useful in workflow design?
Answer: AWS Step Functions dynamic parallelism refers to the ability to process multiple items concurrently within a single state machine execution. Dynamic parallelism can be useful in workflow design when you need to perform the same operation on a collection of items, and the processing of each item can be done independently. By using dynamic parallelism, you can improve the performance of your workflow by processing items concurrently, reducing the overall execution time.
In AWS Step Functions, you can achieve dynamic parallelism using the Map
state. The Map
state allows you to process an array of items in parallel by applying the same state machine (defined in the Iterator
field) to each item.
12. How would you create a simple AWS Step Function workflow using the AWS Management Console? Please provide the necessary steps.
Answer: To create a simple AWS Step Function workflow using the AWS Management Console, follow these steps:
- Sign in to the AWS Management Console and navigate to the Step Functions console.
- Click on “Create state machine” to start the creation process.
- Choose between “Author with code snippets” or “Author with visual workflow.” For this example, choose “Author with code snippets.”
- In the “State machine definition” text area, provide a simple JSON or YAML state machine definition. For example:
{"Comment":"A simple Hello World example","StartAt":"HelloWorld","States":{"HelloWorld":{"Type":"Pass","Result":"Hello, World!","End":true}}}
- Provide a name for your state machine in the “State machine name” field.
- Choose an IAM role for your state machine, or create a new role if necessary.
- Click on “Create state machine” to create the state machine.
- Once the state machine is created, you can start a new execution by clicking on the “Start execution” button.
- Optionally, provide an execution name and input JSON, then click on “Start Execution.”
- Monitor the execution’s progress and results on the “Execution details” page.
By following these steps, you can create a simple AWS Step Function workflow using the AWS Management Console.
13. AWS Step Functions Cost Optimization – What are some strategies to reduce AWS Step Functions costs?
Answer: To reduce AWS Step Functions costs, implement these strategies:
- Minimize state transitions: Simplify logic and combine states when possible to minimize the number of states in your workflow.
- Reduce execution duration: Optimize workflow processing time by minimizing each state’s processing time and using parallelism for concurrent tasks.
- Choose the right workflow type: Select between Standard and Express Workflows based on cost, execution duration, and state transitions.
- Optimize Lambda function usage: Adjust memory size, set appropriate timeout values, and use provisioned concurrency to optimize Lambda functions’ performance and cost.
- Monitor and analyze costs: Use AWS Cost Explorer, AWS Budgets, and Amazon CloudWatch to monitor and control AWS Step Functions usage and costs.
By implementing these strategies, you can effectively reduce AWS Step Functions costs while maintaining efficient and reliable workflows.
Related Reading: Mastering AWS Step Functions Pricing & Cost Optimization Strategies
14. Can you describe the use of AWS Step Functions in coordinating multiple AWS services for Java applications?
This question also appears in AWS Interview Questions for Java Developers
AWS Step Functions is a serverless workflow service that enables you to coordinate multiple AWS services and manage the application’s flow using state machines. It allows you to build, visualize, and run complex, multi-step workflows that involve various AWS services, such as AWS Lambda, Amazon S3, and Amazon DynamoDB.
To use AWS Step Functions in your Java applications, follow these steps:
Step 1 – Define a state machine: Design a state machine using the Amazon States Language, which describes the sequence of steps, branching logic, parallel execution, and error handling in your workflow.
Step 2 – Create a Step Functions client: In your Java application, create a Step Functions client using the AWS SDK for Java.
import software.amazon.awssdk.services.sfn.SfnClient; SfnClient sfn = SfnClient.builder() .region(Region.US_WEST_2) .build();
Step 3 – Start an execution: Use the StartExecutionRequest
and StartExecutionResponse
classes to start a new execution of your state machine.
import software.amazon.awssdk.services.sfn.model.StartExecutionRequest; import software.amazon.awssdk.services.sfn.model.StartExecutionResponse; String stateMachineArn = "your-state-machine-arn"; String input = "{\"key\": \"value\"}"; StartExecutionRequest startRequest = StartExecutionRequest.builder() .stateMachineArn(stateMachineArn) .input(input) .build(); StartExecutionResponse startResponse = sfn.startExecution(startRequest);
Step 4 – Monitor the execution: Use the DescribeExecutionRequest
and DescribeExecutionResponse
classes to monitor the status and result of the execution.
import software.amazon.awssdk.services.sfn.model.DescribeExecutionRequest; import software.amazon.awssdk.services.sfn.model.DescribeExecutionResponse; String executionArn = startResponse.executionArn(); DescribeExecutionRequest describeRequest = DescribeExecutionRequest.builder() .executionArn(executionArn) .build(); DescribeExecutionResponse describeResponse = sfn.describeExecution(describeRequest);
Related Reading: Cloud Architect Interview Questions & Answers
This concludes the AWS Step Functions Interview Questions and Answers article. With this comprehensive list of basic and advanced questions, you should be well-prepared for your next AWS Step Functions interview.
For more AWS-related interview questions, check out our other resources, such as Top 25 AWS S3 Interview Questions and Answers, AWS Lambda Interview Questions, and Cloud Architect Interview Questions and Answers.