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Top 20 AWS Lambda Interview Questions and Answers for 2024

AWS Lambda is a serverless compute service and an increasingly common subject in technical interviews. Whether you're new to cloud computing or a seasoned pro, understanding AWS Lambda is essential.
May 2024  · 12 min read

One of the most important AWS services to understand is AWS Lambda—a serverless compute service that runs code in response to events and automatically manages the underlying resources.

As Lambda adoption grows, it's common to encounter AWS Lambda questions in technical interviews.

This guide simplifies AWS Lambda interview preparation by providing a curated list of questions and answers. We cover the basics, and we delve into advanced and practical scenarios.

If you want to learn more about AWS, check out this course on AWS Cloud Technology and Services.

Why AWS?

Before exploring the interview questions and answers, it's important to understand why the AWS Cloud is the main go-to platform—this itself might be a question in the interview.

Let’s start by considering the following graph:

Source (Statista)

The graph illustrates AWS's dominance in the global cloud infrastructure services market in Q2 2023. With a 32% market share, AWS is the clear leader, well ahead of its closest competitor, Microsoft Azure, which holds 22% of the market. While other major players like Google Cloud (11%) and Alibaba Cloud (4%) contribute to the market, their shares pale compared to AWS and Azure.

The total revenue generated by cloud infrastructure services in Q2 2023, amounting to $65 billion, further emphasizes this market's growing importance and financial significance. AWS's leading position in this thriving sector reinforces its strategic importance for businesses seeking reliable and scalable cloud solutions.

If you want to learn more about how different cloud providers compare, check out this cheat sheet on AWS, Azure, and GCP Service Comparison.

While financial aspects are significant, AWS's appeal extends beyond market share, encompassing a vast selection of services, reliability, scalability, global reach, robust security, continuous innovation, and a supportive community. These combined factors make AWS expertise a highly sought-after skill in the tech industry, presenting numerous career opportunities for those proficient in this leading cloud platform.

Let’s take a look at a few interview questions!

Basic AWS Lambda Interview Questions

Let's begin with the basics. Whether you're new to AWS Lambda or simply brushing up, these fundamentals will prepare you for a deeper dive into Lambda's capabilities.

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda runs code only when needed and scales automatically, from a few requests per day to thousands per second.

The main components of a Lambda function are:

  • Handler: This is the entry point for our function, a method in our code that processes the invocation event. Think of it as the "main" function of our Lambda code.
  • Event: This is the JSON-formatted input data that triggers the function's execution. It carries information about what initiated the function call.
  • Context: This is an object containing runtime information about the function's execution environment. It includes details like function name, version, memory limits, request ID, and remaining execution time.
  • Environment variables: These are key-value pairs that you can set to configure your Lambda function's behavior without modifying the code itself. They are often used to store API keys, database credentials, or other settings.

Lambda natively supports Node.js, Python, Ruby, Java, Go, C#, and PowerShell. This means we can write our Lambda functions directly in these languages without additional setup.

Additionally, Lambda allows us to use custom runtimes, providing the flexibility to package our function code and dependencies in a container image. This enables support for virtually any programming language, allowing us to choose the tool that best suits our needs.

There are several ways to create Lambda functions:



Best Suited For

Lambda Console

Write code directly in the browser's editor.

Simple functions, quick testing

Deployment Package

Package code and dependencies into a ZIP archive and upload.

Larger projects, complex functions

Container Image

Package function as a Docker container image.

Custom runtimes, specific configurations


Define functions and resources in declarative code using AWS SAM, CloudFormation, or CDK.

Managing complex serverless applications, automation

We can invoke Lambda functions in several ways:

  1. Synchronous invocation: The client waits for the function to complete and return a response.
  2. Asynchronous invocation: The client doesn't wait for a response—Lambda executes the function in the background.
  3. Event source mapping: Lambda automatically polls services like DynamoDB or Kinesis and invokes functions based on events.
  4. Other methods: These include API Gateway, AWS SDKs, or scheduled invocations via Amazon EventBridge.

Intermediate AWS Lambda Interview Questions

There are a few options for packaging dependencies with Lambda code:

  • Direct inclusion: For interpreted languages, we can put dependency files along with our function code in the .zip deployment package.
  • Lambda layers: For compiled languages or larger dependencies, we can use Lambda layers to separately package and share common dependencies across functions.
  • Container images: We can also package dependencies in container images along with the Lambda function code and a custom runtime.

We have several options to optimize Lambda functions:

  1. Memory allocation: Choosing the right memory size is crucial for balancing cost and performance. Tools like AWS Lambda Power Tuning can help find the optimal configuration.
  2. Package size: Smaller function packages lead to faster cold starts (the time it takes for a Lambda function to initialize on its first invocation). Minimize package size by removing unnecessary dependencies and compressing code.
  3. Lambda SnapStart: This feature pre-initializes function environments, significantly reducing cold start times for specific runtimes.
  4. Provisioned concurrency: Configure provisioned concurrency to keep function instances warm, ensuring consistent response times for critical workloads.
  5. Timeouts and concurrency limits: Set appropriate timeouts and reserved concurrency to prevent runaway functions and maintain a stable Lambda environment.

Lambda automatically sends metrics to Amazon CloudWatch, including number of requests, latency, error rates, and more.

We can use CloudWatch Logs to access logs that our function code and the Lambda runtime generate.

For tracing and troubleshooting the performance of distributed Lambda applications, we can use AWS X-Ray.

Lambda extensions enable us to enhance our functions by integrating with monitoring, observability, security, and governance tools.

Extensions can run as separate processes in the execution environment to capture diagnostic information or send data to custom destinations.

Examples include the Datadog extension for metrics and traces, and the AWS AppConfig extension for dynamic configuration updates.

An event source mapping is a Lambda resource that reads items from an event source and invokes a function.

We can use event source mappings to process items from Amazon DynamoDB streams, Amazon Kinesis streams, Amazon MQ queues, self-managed Apache Kafka, Amazon SQS queues, and more.

Lambda provides a polling mechanism to read batches of records from the event sources and invoke a function.

Advanced AWS Lambda Interview Questions

AWS Lambda uses IAM (Identity and Access Management) to control access at two levels:

  • Resource-based policies specify which AWS accounts, services, and resources are allowed to invoke the function.
  • The function's execution role determines what AWS services and resources the function code can access. Following the principle of least privilege, these policies should be as restrictive as possible while still allowing the function to perform its intended tasks.

Cold starts occur when Lambda needs to initialize a new execution environment to process an invocation request. To minimize cold starts and improve our Lambda function's responsiveness, we can employ several strategies:

  1. Utilize SnapStart: This feature (available for certain runtimes) allows us to persist initialized environments, significantly reducing startup times for subsequent invocations.
  2. Enable provisioned concurrency: By keeping a pool of initialized environments ready, we can eliminate cold starts for predictable workloads.
  3. Optimize package size: Reducing the size of our function package by removing unnecessary dependencies and optimizing code can accelerate the initialization process.
  4. Language choice: Opting for languages like Go or Rust, known for faster startup times than JVM languages, can also help mitigate cold start delays.
  5. Execution context reuse: Keeping our Lambda function code separate from the initial setup logic allows us to reuse the execution context between invocations, further reducing overhead.

When exposing Lambda functions via API Gateway, some security best practices include:

  • Using IAM or Lambda authorizers to authenticate and authorize requests.
  • Enabling Amazon Cognito user pools for user management.
  • Defining resource policies to allow or deny access based on request properties like source IPs.
  • Setting up mTLS for secure client-server communication.
  • Using AWS WAF to protect against common web exploits targeting APIs.

Lambda container images allow packaging function code and dependencies in an OCI-compatible container format. Let’s compare container images with .zip deployment packages:


Container Images

.zip Deployment Packages

Runtime Flexibility

Bring our own runtime (any language or version)

Limited to Lambda's pre-defined runtimes

Deployment Size

Up to 10GB

Up to 250MB (unzipped)

Deployment & Cold Start

Slower deployments and potentially higher cold start latency

Faster deployments and generally lower cold start latency

Tooling & Workflow

Seamless integration with our existing container tooling and workflows

May require additional tooling for dependency management and deployment

Best Suited For

Applications with large dependencies or custom runtime requirements

Smaller applications and those compatible with Lambda's pre-defined runtimes

Yes, Lambda functions can invoke other functions directly using the AWS SDK. Common use cases include function orchestration, aggregating results from multiple functions, and fanning out event processing.

When a Lambda function invokes another, the invoked function's resource-based policy should explicitly grant access to allow invocation from the calling function.

Practical AWS Lambda Interview Questions

We can implement a simple REST API using Lambda and API Gateway by taking the following steps:

  1. Create a Lambda function: Develop a Lambda function that receives input from API Gateway as an event object and returns a response object with data and status codes.
  2. Create an API Gateway: In the API Gateway, we create a new REST API with a resource and method corresponding to the API path and the HTTP method.
  3. Configure the integration: We configure the method's integration type as Lambda proxy and specify the function to invoke
  4. Deploy the API: Deploy the API to generate a public URL endpoint for access.
  5. Test thoroughly: We can use tools like cURL or Postman to ensure the API functions correctly, with the Lambda function handling requests and returning appropriate responses.

We can configure a Lambda function to process events from an S3 bucket by taking the steps below:

  1. We create a Lambda function with the appropriate permissions to access the S3 bucket.
  2. In the S3 console, we configure an event notification on the source bucket.
  3. We choose the event types to trigger the notification, such as object creation or deletion.
  4. We specify the Lambda function as the notification destination.
  5. We test by performing actions on the S3 bucket that match the configured event types.
  6. We verify that the Lambda function is invoked with an event containing details of the S3 action.

We can configure a Lambda function to write data to a DynamoDB table by performing these six steps:

  1. We create a DynamoDB table with an appropriate primary key and any required secondary indexes.
  2. We create an IAM role for the Lambda function with permissions to access DynamoDB.
  3. We create a Lambda function and attach the IAM role.
  4. We use the DynamoDB SDK to create a client instance with the table name.
  5. We use the put_item method to write items to the table, specifying the key attributes and other fields.
  6. Test the function with sample events and use DynamoDB queries to verify data is written correctly.

We can implement a scheduled Lambda function by taking these steps–we:

  1. Create a Lambda function to perform the desired task on a schedule.
  2. Open the Amazon EventBridge console and create a new rule.
  3. Define a schedule expression for the rule using rate or cron syntax.
  4. Select the Lambda function as a target for the rule.
  5. Save the rule and test by waiting for the next scheduled event.
  6. Verify the function's execution in CloudWatch metrics and logs.

To shift traffic to a new version of a Lambda function, we:

  1. Publish a new version of the Lambda function with the updated code.
  2. Create an alias that points to the older stable version.
  3. Update the alias to split traffic between the old and new versions using weights (e.g., 90/10).
  4. Gradually adjust the weights to shift more traffic to the new version while monitoring metrics.
  5. Once satisfied, update the alias to send 100% of the traffic to the new version.
  6. Repeat the process for the next function update, treating the previous version as the new stable version.


These common AWS Lambda interview questions test candidates' understanding of core Lambda concepts, best practices, and practical usage patterns.

It's important to remember that hands-on experience building and operating Lambda applications is invaluable for cementing these concepts.

To learn more, check out these two courses:

Photo of Zoumana Keita
Zoumana Keita

Zoumana develops LLM AI tools to help companies conduct sustainability due diligence and risk assessments. He previously worked as a data scientist and machine learning engineer at Axionable and IBM. Zoumana is the founder of the peer learning education technology platform ETP4Africa. He has written over 20 tutorials for DataCamp.


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