Top 25 AWS Lambda Interview Questions (2024 Update)

AWS Lambda has revolutionized how we build and deploy applications in the cloud. As a cornerstone of serverless computing, it allows developers to run code without provisioning or managing servers, enabling them to focus on their application logic rather than infrastructure management. With its growing popularity and wide range of use cases, Lambda has become an essential service in the AWS ecosystem. As a result, if you’re pursuing a career in AWS cloud services, you’ll most likely encounter Lambda-related questions in your interviews.

To help you excel in these interviews and showcase your expertise, we’ve put together a comprehensive list of AWS Lambda interview questions. These questions cover a wide range of topics, from basic concepts to advanced features, architectural considerations, and hands-on practitioner knowledge. 

Whether you are a fresher or experienced professional, by studying these questions and understanding the underlying concepts, you’ll be well-equipped to tackle Lambda related interview questions in your AWS job interviews and prove your proficiency in serverless computing.

Table of Contents

1. What is AWS Lambda, and what are its key features?

AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS) that allows you to run your code without provisioning or managing servers. Key features include:

  • Automatic scaling: Lambda automatically scales your applications based on the number of requests
  • Subsecond metering: You only pay for the compute time you consume
  • Event-driven: Lambda functions can be triggered by various AWS services or custom events
  • Stateless: Each Lambda function is stateless, making them highly concurrent and parallelizable
  • Language support: Supports a wide range of programming languages, including Python, Node.js, Java, and more

2. How does AWS Lambda pricing work?

AWS Lambda pricing is based on the number of requests and the duration of function execution. In AWS Lambda, you are billed for:

  • Number of requests: The total number of requests across all your functions
  • Execution Duration: The time it takes for your code to execute, rounded up to the nearest 100 milliseconds

AWS also offers a free tier with 1 million requests and 400,000 GB-seconds of compute time per month.

3. What are the primary components of an AWS Lambda function?

The primary components of an AWS Lambda function are:

  • Function code: The code that will be executed when the function is triggered
  • Handler: A function in your code that AWS Lambda can invoke when the service executes your code
  • Runtime: The language-specific environment in which your code runs
  • Trigger: An AWS service or resource that starts the execution of a Lambda function
  • Role: An AWS Identity and Access Management (IAM) role that grants your function the necessary permissions to access AWS resources

4. What languages are supported by AWS Lambda?

AWS Lambda supports the following programming languages:

  • Node.js
  • Python
  • Java
  • Go
  • PowerShell
  • Ruby
  • C#

For a complete list of supported language versions, visit the AWS Lambda Developer Guide.

5. How does AWS Lambda handle function concurrency and scaling?

AWS Lambda automatically scales your applications by running your functions concurrently in response to multiple triggers. 

Concurrency is the number of requests being processed by your Lambda function at any given time. AWS Lambda manages scaling by adjusting the number of instances of your function that are available to handle incoming requests.

You can also set reserved concurrency for a function, which guarantees a specific number of concurrent executions for that function. This can help manage function performance and control costs.

6. Explain the difference between AWS Lambda and EC2.

AWS Lambda and Amazon EC2 (Elastic Compute Cloud) are both compute services, but they serve different purposes:

  • AWS Lambda: A serverless compute service that automatically manages the underlying compute resources, allowing you to focus on your code. It’s ideal for event-driven architectures and short-lived tasks.
  • Amazon EC2: A virtual server in the cloud that provides more control over the underlying infrastructure, such as the operating system, networking, and storage. It’s suitable for long-running applications, complex architectures, and applications requiring high performance.

7. What is the significance of the /tmp directory in AWS Lambda functions?

The /tmp directory in AWS Lambda is a temporary storage space available to each function instance. It provides up to 512 MB of disk space for storing temporary files or data during the function’s execution. The contents of the /tmp directory are preserved across multiple invocations of the same function instance but are deleted when the function instance is terminated or replaced.

Here’s an example of how to use the /tmp directory in a Python Lambda function:

import os
import tempfile

def lambda_handler(event, context):

    temp_file = tempfile.NamedTemporaryFile(dir='/tmp', delete=False)
    temp_file.write(b"Temporary data stored in /tmp")
    temp_file.close()

    with open(temp_file.name, 'r') as file:
        data = file.read()
        print(f"Read data from {temp_file.name}: {data}")
    os.remove(temp_file.name)
    return {"message": "Data processed successfully"}

8. How can you deploy an AWS Lambda function using AWS SAM or Serverless Framework?

To deploy an AWS Lambda function using the AWS Serverless Application Model (SAM) or Serverless Framework, follow these steps:

Deploying Lambda function Using AWS SAM

  1. Install the AWS SAM CLI.
  2. Create a SAM template (e.g., template.yaml) that defines your Lambda function and its properties.
  3. Run sam build to build your Lambda function and its dependencies.
  4. Run sam package to package your application artifacts.
  5. Run sam deploy to deploy your application to AWS.

Here’s a sample template.yaml for a Python Lambda function:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: my_function/
      Handler: app.lambda_handler
      Runtime: python3.8

Deploying Lambda function Using Serverless Framework:

  1. Install the Serverless Framework CLI.
  2. Create a serverless.yml file that defines your Lambda function and its properties.
  3. Run serverless deploy to package and deploy your application to AWS.

Here’s a sample serverless.yml for a Python Lambda function:

service: my-service
provider:
  name: aws
  runtime: python3.8
functions:
  myFunction:
    handler: app.lambda_handler

9. What are the different triggers available for AWS Lambda functions?

AWS Lambda functions can be triggered by various AWS services and custom events. Some common triggers include:

  • Amazon API Gateway
  • Amazon S3 (S3 Interview Questions)
  • Amazon DynamoDB
  • Amazon Kinesis Data Streams
  • Amazon Simple Notification Service (SNS)
  • Amazon Simple Queue Service (SQS)
  • AWS Step Functions
  • CloudWatch Events
  • AWS IoT Core

For a complete list of supported event sources, visit the AWS Lambda Developer Guide.

10. How do you set up error handling and retry behavior for AWS Lambda functions?

AWS Lambda provides built-in error handling and retry behavior depending on the type of invocation:

  • Synchronous invocation: In case of an error, AWS Lambda returns the error information directly to the caller. The caller can decide whether to retry or handle the error in another way.
  • Asynchronous invocation: AWS Lambda automatically retries failed invocations twice, with delays between retries. You can also configure a dead-letter queue (using Amazon SQS or SNS) to handle failed invocations.

To add custom error handling to your Lambda function, you can use try-except blocks (for Python) or try-catch blocks (for other languages) in your code. 

You can also use AWS X-Ray for tracing and monitoring the performance of your Lambda functions.

Here’s an example of error handling in a Python Lambda function:

import json
def lambda_handler(event, context):

    try:
        # Your code here
        result = process_event(event)
        return {
            'statusCode': 200,
            'body': json.dumps(result)
        }

    except Exception as e:
        print(f"Error: {str(e)}")
        return {
            'statusCode': 500,
            'body': json.dumps({"error": "An error occurred"})
        }

11. Describe the use of environment variables in Lambda functions.

Environment variables in AWS Lambda functions allow you to pass configuration settings and secrets to your function code without hardcoding them. 

You can configure environment variables in the AWS Management Console, AWS CLI, or your function’s deployment package.

Here’s an example of how to set and access environment variables in a Python Lambda function:

import os
def lambda_handler(event, context):
    api_key = os.environ.get("API_KEY")
    # Use the API key in your function

In the AWS Management Console, you can set the API_KEY environment variable in the “Configuration” tab of your Lambda function.

12. What are the AWS Lambda execution limits and how can they be increased?

AWS Lambda imposes several limits on function configuration, deployment, and execution to ensure the stability and security of the service. Some common limits include:

  • Function memory: 128 MB to 3008 MB, in 64 MB increments
  • Function timeout: 1 second to 15 minutes
  • Function deployment package size: 50 MB (zipped) or 250 MB (unzipped)
  • Concurrency: Default soft limit of 1000 concurrent executions per region

To request an increase in these limits, you can submit a support request through the AWS Support Center.

13. What is the Lambda function cold start and how can it impact performance?

A cold start in AWS Lambda occurs when a new function instance is created to handle a request. Cold starts can increase the function’s execution time because AWS needs to set up the runtime environment, initialize the function, and allocate necessary resources.

The impact of cold starts on performance depends on the size and complexity of your function, as well as the resources it requires. To minimize cold start latency, you can:

  • Use provisioned concurrency to keep a specified number of function instances warm and ready to serve requests
  • Optimize the function’s initialization code and reduce package size
  • Adjust the function’s memory and timeout settings for better performance
  • Use AWS Lambda Extensions to offload initialization tasks

Here’s an example of how to set up provisioned concurrency for your Lambda function using AWS SAM:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: my_function/
      Handler: app.lambda_handler
      Runtime: python3.8
      ProvisionedConcurrencyConfig:
        ProvisionedConcurrentExecutions: 5

14. What is the difference between AWS Lambda and AWS Step Functions?

AWS Lambda and AWS Step Functions are both serverless services in AWS, but they serve different purposes:

  • AWS Lambda: A serverless compute service that lets you run your code in response to events without managing servers. It’s ideal for event-driven architectures and short-lived tasks.
  • AWS Step Functions: A serverless workflow service that lets you coordinate distributed applications using visual workflows. It enables you to build complex, multi-step processes that involve multiple AWS services, conditional logic, and error handling.

For more information on AWS Step Functions and how to use it with Lambda functions, refer to the AWS Step Functions Developer Guide.

15. How can you secure your AWS Lambda functions?

Securing your AWS Lambda functions involves several best practices and features, including:

  • IAM roles: Assign least-privileged IAM roles to your Lambda functions to control their access to AWS resources
  • VPC: Run your Lambda functions in a Virtual Private Cloud (VPC) for network isolation and to restrict access to specific resources
  • Encryption: Use AWS Key Management Service (KMS) to encrypt environment variables and data at rest, and use HTTPS for data in transit
  • Monitoring: Use Amazon CloudWatch, AWS X-Ray, and AWS CloudTrail to monitor function performance, trace requests, and log API calls
  • Security patches: Regularly update your function’s runtime and dependencies to address security vulnerabilities

16. How can you test AWS Lambda functions locally and in the AWS Management Console?

To test AWS Lambda functions locally, you can use the AWS SAM CLI or the Serverless Framework CLI:

  • AWS SAM: Use sam local invoke to run your Lambda function locally in a Docker container that simulates the AWS Lambda environment.
  • Serverless Framework: Use serverless invoke local to run your Lambda function locally without a Docker container.

Here’s an example of how to test a Python Lambda function locally using AWS SAM:

$ sam local invoke MyFunction -e event.json

To test your Lambda function in the AWS Management Console, you can create a test event with sample input data and invoke the function directly:

  1. Open the AWS Management Console and navigate to the Lambda service.
  2. Select your function from the list of functions.
  3. Click on the “Test” button in the upper-right corner.
  4. Configure a new test event with sample input data, and click “Create”.
  5. Click “Test” again to invoke your function with the selected test event.

The test results, including the function’s output and execution duration, will be displayed in the console.

17. How can you monitor and debug AWS Lambda functions?

To monitor and debug AWS Lambda functions, you can use the following AWS services and tools:

  • Amazon CloudWatch: Collect metrics, logs, and set alarms for your Lambda functions. Use CloudWatch Logs Insights to analyze and visualize log data.
  • AWS X-Ray: Trace and analyze the performance of your Lambda functions and their downstream services. Use X-Ray to identify bottlenecks and errors in your application.
  • AWS CloudTrail: Log, monitor, and retain account activity related to your Lambda functions, including function invocations and management events.
  • AWS Lambda Extensions: Use Lambda extensions to integrate your functions with third-party monitoring, observability, and security tools.

Here’s an example of how to enable X-Ray tracing for a Python Lambda function using AWS SAM:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: my_function/
      Handler: app.lambda_handler
      Runtime: python3.8
      Tracing: Active

18. What is the difference between AWS Lambda and AWS Fargate?

AWS Lambda and AWS Fargate are both serverless compute services in AWS, but they cater to different types of workloads:

  • AWS Lambda: A serverless compute service designed for event-driven architectures and short-lived tasks. It automatically scales, patches, and manages the compute infrastructure, allowing you to focus on your code.
  • AWS Fargate: A serverless container orchestration service that lets you run containers without managing the underlying infrastructure. It works with Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) to manage the deployment, scaling, and maintenance of containerized applications.

19. How can you optimize AWS Lambda function performance and cost?

To optimize AWS Lambda function performance and cost, consider the following best practices:

  • Adjust memory and timeout settings: Find the right balance between memory and execution time for your function. Higher memory allocations can lead to faster execution times, but also higher costs.
  • Reduce package size: Smaller deployment packages result in faster function initialization times, reducing cold start latency.
  • Use provisioned concurrency: Configure a certain number of function instances to be always warm and ready to serve requests, minimizing cold start latency.
  • Optimize function code: Improve the efficiency of your function code by using efficient algorithms, minimizing blocking I/O operations, and using connection pooling for external services.
  • Use caching: Cache frequently used data in your function to reduce the need for external requests and speed up execution.
  • Monitor and analyze function metrics: Use Amazon CloudWatch, AWS X-Ray, and AWS Cost Explorer to monitor function performance, identify bottlenecks, and analyze cost trends.

20. How can you integrate AWS Lambda with other AWS services?

AWS Lambda can be integrated with a wide range of AWS services for event-driven architectures, data processing, and application workflows. Some common integrations include:

  • Amazon API Gateway: Create RESTful APIs that trigger your Lambda functions in response to HTTP requests.
  • Amazon S3: Trigger Lambda functions when new objects are created or deleted in an S3 bucket.
  • Amazon DynamoDB: Invoke Lambda functions in response to DynamoDB table events, such as item inserts, updates, or deletes.
  • Amazon Kinesis Data Streams: Process streaming data in real-time by invoking Lambda functions for each data record in a Kinesis stream.
  • AWS Step Functions: Coordinate multi-step workflows involving Lambda functions and other AWS services using visual workflows.

Here’s an example of how to configure an S3 event trigger for a Lambda function using AWS SAM:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: my_function/
      Handler: app.lambda_handler
      Runtime: python3.8
      Events:
        S3Event:
          Type: S3
          Properties:
            Bucket: my-bucket
            Events: s3:ObjectCreated:*

21. What is AWS Lambda@Edge, and how does it differ from regular AWS Lambda?

AWS Lambda@Edge is a feature of Amazon CloudFront that lets you run AWS Lambda functions at CloudFront edge locations. It allows you to process HTTP requests and responses at the edge, closer to your users, to reduce latency and improve performance.

Some key differences between AWS Lambda@Edge and regular AWS Lambda include:

  • Geographical distribution: Lambda@Edge functions are distributed across CloudFront’s global edge locations, while regular Lambda functions run in a specific AWS region.
  • Event sources: Lambda@Edge functions are triggered by CloudFront events, such as viewer requests, origin requests, origin responses, and viewer responses. Regular Lambda functions can be triggered by a wide range of AWS services and custom events.
  • Resource limits: Lambda@Edge has different resource limits compared to regular Lambda, such as lower maximum memory size and function timeout.

22. How can you use AWS Lambda in a VPC?

To use AWS Lambda in a VPC, you need to configure your Lambda function to access resources within the VPC by associating it with one or more VPC subnets and security groups. This allows your Lambda function to access resources, such as Amazon RDS databases or Amazon ElastiCache clusters, within your VPC.

Here’s an example of how to configure a Lambda function to run in a VPC using AWS SAM:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: my_function/
      Handler: app.lambda_handler
      Runtime: python3.8
      VpcConfig:
        SubnetIds:
          - subnet-0123456789abcdef0
          - subnet-0123456789abcdef1
        SecurityGroupIds:
          - sg-0123456789abcdef0

Note that running a Lambda function in a VPC may increase its cold start latency, as it requires setting up additional resources, such as Elastic Network Interfaces (ENIs). To minimize cold start latency, you can use provisioned concurrency or optimize your function’s initialization code.

23. How can you handle errors and retries in AWS Lambda?

To handle errors and retries in AWS Lambda, you can use the following strategies:

  • Error handling in function code: Catch and handle errors in your Lambda function code by using exception handling and error logging.
  • Dead Letter Queues (DLQs): Configure a DLQ, such as an Amazon SQS queue or Amazon SNS topic, to receive unprocessed events after a specified number of retries. This helps you analyze and reprocess failed events.
  • AWS Step Functions: Use AWS Step Functions to create more complex error handling and retry workflows, with features such as catch blocks, custom retry policies, and fallback states.
  • AWS X-Ray: Use AWS X-Ray to trace and analyze the performance of your Lambda functions, identify errors, and visualize the root cause of issues in your application.

Here’s an example of how to configure a Dead Letter Queue for a Lambda function using AWS SAM:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: my_function/
      Handler: app.lambda_handler
      Runtime: python3.8
      DeadLetterQueue:
        Type: SNS
        TargetArn: arn:aws:sns:us-east-1:123456789012:my-topic

24. How can you secure AWS Lambda functions?

To secure AWS Lambda functions, follow these best practices:

  • Least privilege: Grant your Lambda functions the minimum set of permissions required to perform their tasks by using IAM roles and policies.
  • Secure function code: Keep your function code secure by using code signing, validating input data, and following secure coding practices.
  • Function isolation: Run your Lambda functions in a VPC to isolate them from the public internet and control access to VPC resources.
  • Data encryption: Use AWS Key Management Service (KMS) to encrypt sensitive data in your Lambda function code, environment variables, and event data.
  • Logging and monitoring: Use Amazon CloudWatch, AWS CloudTrail, and AWS X-Ray to log, monitor, and analyze your Lambda functions’ activity and performance.
  • Compliance and governance: Use AWS Config, AWS Security Hub, and custom Lambda authorizers to enforce compliance and cloud governance requirements for your Lambda functions. 

25. What are some use cases for AWS Lambda?

AWS Lambda is well-suited for a variety of use cases, including:

  • Event-driven architectures: Trigger Lambda functions in response to events from AWS services or custom applications, such as Amazon S3 object uploads or Amazon DynamoDB table updates. (Related Reading: Cloud Architect Interview Questions)
  • Serverless web applications: Build serverless web applications by using Lambda functions with Amazon API Gateway to create RESTful APIs.
  • Data processing: Process data in real-time or in batches using Lambda functions with services like Amazon Kinesis Data Streams, Amazon S3, and AWS Glue.
  • Microservices: Implement individual microservices as Lambda functions, allowing for independent scaling and deployment of each service in a distributed architecture. (Related Reading: Monolith to Microservices Modernization)
  • Task automation: Automate tasks, such as image resizing, log processing, or data validation, by invoking Lambda functions on a schedule or in response to specific events.

26. Can you explain how to use AWS Cloud Development Kit (CDK) to create and deploy AWS Lambda functions?

AWS Cloud Development Kit (CDK) is an open-source software development framework used to define cloud infrastructure in code and provision it through AWS CloudFormation. CDK uses constructs, which are cloud components that can represent architectures of any complexity. These constructs can encapsulate AWS Lambda functions, allowing us to manage them more effectively.

To use AWS CDK to create and deploy AWS Lambda functions, you’ll need to follow several steps:

  1. Set up your environment: This includes installing Node.js, AWS CDK Toolkit, and initializing a new AWS CDK project.
  2. Write your AWS Lambda function: Depending on your chosen programming language, you’d write your function accordingly.
  3. Create AWS CDK constructs for your AWS Lambda function: The AWS CDK code you write will represent your Lambda function and any related resources.
  4. Build and deploy your AWS CDK App: By synthesizing your CDK application, you generate an AWS CloudFormation template. You then use AWS CDK CLI to deploy the template and thus your Lambda function.
  5. Update and manage your Lambda functions: AWS CDK makes it easy to update your Lambda function code and manage versions and aliases.

For a more detailed explanation and step-by-step walkthrough, you can refer to our comprehensive guide on Using AWS CDK for Lambda Infrastructure Automation.

Conclusion

This article has provided you with a comprehensive set of AWS Lambda interview questions, covering basic concepts, advanced topics, architectural considerations, and hands-on practitioner knowledge. We’ve included relevant data and links to help you prepare for your interview and gain a deep understanding of AWS Lambda.

Remember to review the AWS Lambda Developer Guide, AWS Serverless Application Model (SAM) documentation, and other AWS Well-Architected resources for more detailed information on specific topics.

As you prepare for your interview, make sure to practice answering questions with code snippets and examples and to familiarize yourself with the integration of AWS Lambda with other AWS services.