Top 10 AWS Lambda Interview Questions for 2024

AWS, the leading cloud service provider, provides a wide range of services that allow organizations to scale and innovate at an unprecedented pace. Among these services, AWS Lambda stands out as a tool for serverless computing, allowing developers to run code without having to set up or manage servers.

AWS Lambda runs your code reacting to events like HTTP requests, data updates, and system status changes, making it an indispensable tool for developing flexible, scalable, and cost-effective applications. AWS Lambda frees developers from the burden of infrastructure management, allowing them to focus on writing and optimizing code.

This article will provide you with the top 10 AWS Lambda interview questions for 2024. As the relevance of data engineering grows, mastering AWS Lambda can greatly raise your skill set and employment market value. 

Whether you’re aiming to land your dream data engineering job or looking to advance your career, understanding AWS Lambda is a step toward achieving your goals. At Data Engineer Academy, we are dedicated to providing you with the tools and knowledge necessary to succeed in the competitive field of data engineering.

Top 10 AWS Lambda Interview Questions

Let’s break down some of the most frequent interview questions you may get while addressing AWS Lambda in a data engineering interview. These questions are intended to assess your knowledge of key principles, best practices, and real-world uses of AWS Lambda.

1. What is AWS Lambda, and how does it work?

When asked “What is AWS Lambda?” in an interview, it’s important to provide a comprehensive yet concise explanation highlighting its core features and benefits.

AWS Lambda is a powerful serverless computation technology from AWS that transforms the way you install and run applications. Instead of managing servers or expanding infrastructure, Lambda allows you to focus solely on developing code.

How does AWS Lambda work?

Serverless execution
With AWS Lambda, you don’t need to provision or manage servers. AWS handles everything, from infrastructure management to scaling. This means you can deploy your functions and AWS automatically ensures they run in a highly available environment.

Event-driven model
Lambda is designed to be event-driven. It responds to events from various AWS services. For example, it can execute code in response to HTTP requests via API Gateway, file uploads to S3, changes in DynamoDB tables, or even scheduled events. This makes it extremely versatile for building reactive applications.

Automatic scaling
One of the standout features of Lambda is its ability to scale automatically. When an event triggers your function, AWS Lambda launches as many instances of the function as needed to handle the load. This ensures that your application can handle a high volume of requests without manual intervention.

Support for multiple languages
AWS Lambda supports several programming languages, including Node.js, Python, Ruby, Java, Go, and .NET Core. This flexibility allows you to use the language and runtime that best fit your project requirements.

Seamless integration with AWS services
Lambda integrates natively with a wide range of AWS services. Whether you need to trigger functions from S3, interact with DynamoDB, or process streams from Kinesis, Lambda’s tight integration within the AWS ecosystem makes it an ideal choice for building complex applications.

2. Can you describe the limitations of AWS Lambda?

When describing the limitations of AWS Lambda in an interview, it is important to provide a clear and concise overview of the key constraints and considerations. 

While AWS Lambda offers numerous benefits, there are several limitations to be aware of:

Execution Limits

• The maximum execution timeout for a Lambda function is 15 minutes. This means long-running processes are not suitable for Lambda.
• Lambda functions can be allocated between 128 MB and 10 GB of memory. The CPU and other resources are allocated in proportion to the memory size.
• The deployment package, including the code and dependencies, has a size limit of 50 MB (zipped) and 250 MB (unzipped).

Operational limits –  cold starts can introduce latency, especially for functions not invoked frequently. This is due to the time taken to initialize the execution environment.

Language and runtime limits – language Support: While Lambda supports several popular languages (Node.js, Python, Ruby, Java, Go, .NET Core), it may not support all the features or libraries of these languages, and some languages may have performance differences.

Security and Permissions

• Lambda requires properly configured IAM roles and permissions. Misconfigured permissions can lead to security vulnerabilities or function failures.
• When running Lambda functions inside a VPC, you need to manage VPC configurations, such as subnet and security group settings, which can complicate deployments.

Integration limits – while Lambda integrates with many AWS services, there are limitations in how these integrations can be configured and managed. For example, there might be throttling limits on services like API Gateway or S3 when invoking Lambda functions.

3. How do you monitor and troubleshoot AWS Lambda functions?

Monitoring AWS lambda functions

Amazon CloudWatch

• AWS Lambda automatically generates log entries for each invocation, which are stored in Amazon CloudWatch Logs. You can view these logs to understand the behavior of your functions and diagnose issues.
• CloudWatch provides several built-in metrics for Lambda functions, such as invocation count, error count, duration, and throttles. These metrics help you monitor the performance and resource usage of your functions.

AWS X-Ray

AWS X-Ray helps you analyze and debug distributed applications by providing end-to-end tracing. You can use X-Ray to trace requests as they travel through your Lambda functions and other AWS services, identifying bottlenecks and performance issues.

AWS Lambda Console

The Lambda console’s monitoring tab provides a visual representation of key metrics, including invocations, errors, duration, and concurrency. This helps you quickly assess the health of your functions.

Troubleshooting AWS Lambda Functions

1. Reviewing CloudWatch logs
   • Check CloudWatch Logs for error messages and stack traces that can provide insights into what went wrong during the execution of your function.
   • Implement custom logging within your Lambda function code to capture additional information, such as variable values or specific execution paths. Use the console.log() function (Node.js) or print() (Python) to output log messages.
2. Analyzing CloudWatch metrics
   • High error rates or increased invocation durations can indicate issues with your Lambda function. Use these metrics to identify problematic functions.
   • Monitor throttling metrics to ensure your functions are not being throttled due to hitting concurrency limits. Adjust the concurrency settings or optimize your code to reduce execution time.
3. Using AWS X-Ray
   • The X-Ray service map helps visualize the flow of requests through your application, showing latencies and error rates for each component. This is useful for identifying where issues may be occurring in your serverless architecture.
   • Analyze X-Ray segments and subsegments to get detailed information about each request and the operations performed by your Lambda function. This helps pinpoint specific areas of your code that may be causing problems.
4. Performance tuning
   • Adjust the memory allocation for your Lambda function based on its performance. Increasing memory can also improve CPU performance, potentially reducing execution time.
   • Ensure your function’s timeout setting is appropriate for its expected execution duration. Functions timing out frequently may need optimization or an increased timeout limit.
5. Dependency management
   • Keep your deployment package size small by including only necessary dependencies. Large packages can increase cold start times.
   • Verify that any third-party libraries you use are compatible with AWS Lambda and do not introduce performance issues.

4. What are some best practices for securing AWS Lambda functions?

Securing AWS Lambda functions is important for the integrity and safety of your applications.

Principle of least privilege

Firstly, restrict IAM permissions to ensure that each Lambda function has the minimum necessary access to operate. This involves creating specific IAM roles with tailored permissions, avoiding overly broad permissions that could expose unnecessary resources.

Environment security

Store sensitive information, such as API keys and database credentials, in environment variables, and use AWS Key Management Service (KMS) to encrypt these variables. Regularly review and update your Lambda function configurations to ensure sensitive data is not hardcoded in your source code.

Network security

When your Lambda function needs to access resources within a Virtual Private Cloud (VPC), configure security groups and network ACLs appropriately to restrict inbound and outbound traffic. Avoid exposing Lambda functions directly to the internet by using API Gateway or Application Load Balancer to manage and secure access.

Code security

Use static code analysis tools to scan your Lambda function code for vulnerabilities before deployment. Regularly update and patch third-party libraries and dependencies to mitigate known vulnerabilities.

Logging and monitoring

Enable logging with Amazon CloudWatch Logs to capture and review logs from your Lambda functions. Implement custom logging for critical operations and set up CloudWatch Alarms and AWS Config Rules to monitor for unusual activity. Use AWS CloudTrail to audit API calls made by or on behalf of your Lambda functions.

Data protection

Encrypt any sensitive data processed or stored by your Lambda functions using AWS KMS for managing encryption keys. Ensure that communication between Lambda functions and other services is secure, preferably using HTTPS.

Auditing and compliance

Perform regular security audits of your Lambda functions and associated resources to ensure compliance with industry standards and best practices. Utilize AWS security services like AWS Security Hub, AWS Config, and AWS Inspector for continuous monitoring and enforcement of security compliance.

5. How do you manage versioning and aliases in AWS Lambda?

Managing versioning and aliases in AWS Lambda is key to deploying, updating, and maintaining Lambda functions efficiently.

Versioning

Versioning in AWS Lambda allows you to publish one or more versions of your Lambda function. Each version is immutable and has a unique ARN (Amazon Resource Name), enabling you to reliably reference specific iterations of your function.

1. After deploying a Lambda function, you can create a version by publishing it. This locks the code and configuration, creating an immutable snapshot of your function. You can do this through the AWS Management Console, AWS CLI, or AWS SDKs.

aws lambda publish-version --function-name my-function

2. Each version is assigned a unique version number (e.g., 1, 2, 3). You can reference these versions in your code or configurations, ensuring that specific environments or services use the exact function version you intend.

Aliases are pointers to specific versions of your Lambda function. They provide a level of abstraction, allowing you to manage different environments (like development, staging, and production) more effectively.

1. Creating Aliases: An alias can be created and mapped to a specific function version. This makes it easier to manage and deploy updates, as you can switch the alias to point to a new version without changing the references in your application code.

aws lambda create-alias --function-name my-function --name PROD --function-version 1

2. Updating Aliases: When you deploy a new version of your Lambda function, you can update the alias to point to the new version. This makes rolling out updates seamless and minimizes downtime.

aws lambda update-alias --function-name my-function --name PROD --function-version 2

3. Traffic Shifting: AWS Lambda allows you to shift traffic between two versions using an alias. This is useful for gradually rolling out updates and testing new versions before full deployment. You can define a percentage of traffic to be sent to the new version while keeping the rest on the old version.

aws lambda update-alias --function-name my-function --name PROD --function-version 2 --routing-config '{"AdditionalVersionWeights": {"1": 0.2}}'

By effectively managing versioning and aliases, you can ensure smooth deployments, maintain application stability, and minimize the risk of disruptions during updates.

6. What are cold starts in AWS Lambda, and how do you mitigate them?

What are cold starts in AWS Lambda?

Cold starts in AWS Lambda refer to the latency that occurs when a function is invoked for the first time or after a period of inactivity. When a Lambda function is called, AWS needs to provision an execution environment, which includes setting up the container, initializing the runtime, and loading the function code and any dependencies. This initial setup introduces a delay, known as a cold start.

Factors affecting cold starts

• Larger functions with more dependencies take longer to load, increasing the cold start time.
• Different runtimes have varying initialization times. For instance, Java and .NET Core typically have longer cold start times compared to Node.js or Python.
• If your Lambda function is configured to run within a VPC, the additional network setup time can increase the cold start duration.

Mitigating cold starts

To reduce the impact of cold starts, you can employ several strategies:

Optimize function code and dependencies

• Reduce the size of your deployment package by including only necessary dependencies. Use tools like AWS Lambda Layers to manage common dependencies separately.
• Move any initialization code outside the main handler to ensure it runs only once per container lifecycle, not on every invocation.

Use provisioned concurrency

• AWS Lambda allows you to pre-warm a specified number of function instances to ensure they are ready to handle requests without delay. This can be particularly useful for latency-sensitive applications.
• You can configure provisioned concurrency via the AWS Management Console, AWS CLI, or AWS SDKs.

aws lambda put-provisioned-concurrency-config --function-name my-function --provisioned-concurrent-executions 5

Keep functions warm

Regularly invoke your Lambda functions using Amazon CloudWatch Events or AWS EventBridge to keep them warm. This reduces the chances of encountering a cold start.
Example: Schedule a CloudWatch Event to trigger your function every few minutes

aws events put-rule --schedule-expression "rate(5 minutes)" --name "keep-warm-rule"

Optimize VPC configuration

• Ensure that your Lambda function is associated with subnets in multiple Availability Zones for better availability and reduced cold start latency.
• Use minimal and efficient security group rules to decrease the setup time for VPC-configured functions.

Use lighter runtimes

Choose runtimes with faster initialization times for latency-sensitive functions. For example, Node.js and Python typically have shorter cold start times compared to Java or .NET Core.

By implementing these strategies, you can effectively mitigate the impact of cold starts on your AWS Lambda functions, ensuring better performance and responsiveness for your serverless applications.

7. How do you integrate AWS Lambda with other AWS services?

Integrating AWS Lambda with other AWS services involves using these services as triggers or targets for Lambda functions. For example, you can trigger a Lambda function with an S3 event whenever a file is uploaded to a bucket. API Gateway can be used to invoke Lambda functions in response to HTTP requests, enabling you to build serverless APIs. DynamoDB Streams can trigger Lambda functions when data changes in a DynamoDB table, allowing real-time processing of database updates. Additionally, you can use SNS and SQS to trigger Lambda functions for message processing, and CloudWatch Events or EventBridge to schedule regular function invocations or respond to system events. These integrations enable Lambda functions to seamlessly interact with other AWS services, creating a highly modular and scalable architecture.
Data Engineering Academy AWS course provides comprehensive training on AWS Lambda and other AWS services, preparing you for real-world applications and technical interviews.

8. What are the best practices for writing AWS Lambda functions?

Writing efficient and reliable AWS Lambda functions requires adhering to several best practices to ensure optimal performance, maintainability, and security:

Keep functions lightweight

Include only essential libraries and dependencies in your deployment package. Use AWS Lambda Layers to manage common dependencies separately and reduce package size. Keep your function code simple and focused on a single responsibility. This makes it easier to manage, debug, and scale.

Efficient resource management

Allocate the appropriate amount of memory to your Lambda functions based on their workload. Remember that more memory also means more CPU power, which can reduce execution time. Use environment variables to manage configuration settings and secrets, rather than hardcoding them in your code.

Logging and monitoring

Implement logging to capture useful information for troubleshooting and monitoring, but avoid excessive logging that can increase costs and clutter CloudWatch Logs.
Utilize AWS CloudWatch to monitor key metrics like invocation count, duration, and error rates. Set up alerts to notify you of any anomalies.

Error handling and retries

Implement comprehensive error handling within your functions to manage exceptions and retries effectively. This includes using try-catch blocks and returning meaningful error messages.
Use AWS Lambda’s built-in retry mechanism for asynchronous invocations, and implement custom retry logic where appropriate for synchronous calls.

Security best practices

Assign the minimum necessary permissions to your Lambda function’s IAM role to reduce security risks. Regularly review and update these permissions. Use AWS Key Management Service (KMS) to encrypt sensitive data, both at rest and in transit. Ensure environment variables storing sensitive information are encrypted.

9. How do you manage deployment and automation for AWS Lambda?

Use infrastructure as code (IaC)

Use AWS CloudFormation to define your Lambda functions and other AWS resources in code. This allows you to deploy and manage your infrastructure consistently and version it alongside your application code.

Resources:
  MyLambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      Handler: index.handler
      Role: arn:aws:iam::123456789012:role/execution_role
      Code:
        S3Bucket: my-bucket
        S3Key: my-function.zip
      Runtime: nodejs14.x 

AWS SAM is an extension of CloudFormation specifically for serverless applications. It simplifies the definition and deployment of Lambda functions, APIs, and other serverless resources.

Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: index.handler
      Runtime: nodejs14.x
      CodeUri: s3://my-bucket/my-function.zip
      Policies: AmazonDynamoDBReadOnlyAccess 

The Serverless Framework is a popular open-source framework for building and deploying serverless applications. It abstracts away much of the complexity and allows you to deploy Lambda functions and their associated resources easily.

service: my-service
provider:
  name: aws
  runtime: nodejs14.x
functions:
  myFunction:
    handler: handler.myFunction
    events:
      - http:
          path: users/create
          method: post

Implement CI/CD pipelines using tools like AWS CodePipeline, Jenkins, GitLab CI, or GitHub Actions to automate the deployment process. These pipelines can build, test, and deploy your Lambda functions automatically whenever changes are pushed to your code repository.

# Example GitHub Actions workflow
name: CI/CD Pipeline

on:
  push:
    branches:
      - main

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout code
        uses: actions/checkout@v2

      - name: Install dependencies
        run: npm install

      - name: Deploy to AWS
        run: serverless deploy
        env:
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

Managing deployment and automation for AWS Lambda functions entails implementing tools and processes that assure rapid, dependable, and repeatable deployments.

10. Can you explain a real-world project where you used AWS Lambda?

At Data Engineer Academy, we believe in learning by doing. Our courses include real project questions and hands-on practice with AWS Lambda, allowing you to apply theoretical knowledge to real-world scenarios.

Our comprehensive course covers a wide spectrum of AWS functionalities. With a unique simulator, you’ll gain hands-on experience, exploring areas such as Aurora Athena using Glue, the intricacies of ELT Pipeline through DMS, mastering AWS Spark EMR & DBT, and more. Learn migration topics like S3 GCP and Cross Region S3-S3 migration with Lambda. And, we’ll conclude with the integration of DBT Postgres with Airflow on Windows, ensuring a holistic understanding.

Conclusion

Mastering AWS Lambda is essential for any aspiring data engineer, as serverless computing continues to play a significant role in modern cloud architectures. By familiarizing yourself with the top 10 AWS Lambda interview questions for 2024, you can gain a solid understanding of key concepts, best practices, and real-world applications. This preparation not only boosts your confidence but also enhances your ability to tackle complex technical interviews effectively.

Chris Garzon

Christopher Garzon has worked as a data engineer for Amazon, Lyft, and an asset management start up where he was responsible for building the entire Data Infrastructure from scratch. He is the author “Ace the Data Engineer Interview” and has helped 100’s of students break into the data engineer industry. He is also an angel investor, an advisor to multiple to multiple start ups, and the founder and CEO of Data Engineer Academy.