Most frequently Asked AWS Lamda Interview Questions

1. What experience do you have with AWS Lambda?
2. How familiar are you with serverless computing?
3. What strategies have you used to optimize the performance of AWS Lambda?
4. Describe a time you encountered an issue while working with AWS Lambda and how you overcame it.
5. How comfortable are you with setting up and configuring AWS Lambda functions?
6. Have you ever had to troubleshoot a problem related to AWS Lambda?
7. What challenges have you faced while developing applications on top of AWS Lambda?
8. How do you ensure that AWS Lambda functions are secure and compliant with company policies?
9. What strategies do you use to ensure the scalability and reliability of AWS Lambda-based applications?
10. What challenges do you think developers will face when working with serverless architectures?
11. Do you have any experience deploying AWS Lambda functions to production environments?
12. In what ways have you used AWS Lambda to optimize cost?

What experience do you have with AWS Lambda?

I have extensive experience working with AWS Lambda.
As a cloud-based, serverless computing platform, AWS Lambda enables developers to run code for virtually any type of application or backend service without having to manage servers.
With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration.
By running your code in the cloud, you have access to the same suite of AWS services and features that power some of the world's largest enterprises.
The basic usage of AWS Lambda functions involves creating an event source that triggers your code, and then executing the code with the code being uploaded to a cloud-hosted environment.
Lambda functions are also triggered by "events sources" such as Amazon Kinesis streams, Amazon S3 buckets, and Amazon DynamoDB tables.
Additionally, the AWS Lambda programming model allows you to organize your code into functions and create triggers with the code.
To show a practical example of how AWS Lambda can be used, here is a code snippet for a simple "Hello World" function using Python:

exports.handler = async (event) => {
    console.log("Hello World!");
    return {
        statusCode: 200,
        body: 'Hello World!'
    };
};

How familiar are you with serverless computing?

I can provide an answer to your question regarding serverless computing.
Serverless computing is a cloud execution model that allows applications to be hosted without having to manage the underlying servers.
It enables developers to focus on their code instead of their infrastructure and allows them to quickly build and deploy applications.
For example, if a developer wants to create an API endpoint to accept HTTP requests, they can use AWS Lambda to set up the endpoint and write the logic to handle the requests.
The code snippet below is an example of how to set up a simple Lambda function using Node.js:

exports.handler = async (event) => {
const response = {
    statusCode: 200,
    body: JSON.stringify('Hello World!'),
};
return response;
};

Serverless computing offers a number of advantages, such as scalability, cost-effectiveness, flexibility, and speed of deployment.
It allows for rapid prototyping, so developers can quickly build and iterate on features.
By removing the need to manage servers, it reduces operational overhead and allows developers to focus on the core product.
Additionally, serverless architectures are highly secure, since they run on managed infrastructure and allow for easy deployment of security measures such as encryption and authentication.
Overall, serverless computing is a great way to simplify the application development process, make applications more efficient, and reduce costs.

What strategies have you used to optimize the performance of AWS Lambda?

When it comes to optimizing the performance of AWS Lambda, there are several strategies that can be employed.
The most common strategies are reducing Lambda function cold starts, optimizing the memory usage, and improving the code efficiency.
Regarding cold starts, it is important to plan ahead of time for how to minimize its impact on the overall performance.
Choosing an appropriate timeout duration and running Lambda functions asynchronously will help reduce latency resulting from cold starts.
Additionally, using aprovisioned concurrency for key Lambda functions can help ensure that they stay warmed up.
Optimizing memory usage requires understanding the dependencies of the Lambda functions, and setting the configured memory and timeout to the minimal values required to ensure optimal performance.
A code snippet that can aid in this optimization process is shown below:

```
# Configure memory and timeout settings
lambertclient = boto3.client('lambda')
response = lambertclient.update_function_configuration(
    FunctionName='my-lambda-function',
    MemorySize= 128,
    Timeout = 3 # seconds
)
```

Finally, when it comes to improving code efficiency, reusing resources and using high-level compilers such as Node.
js or Python's Numba can speed up the code execution drastically.
Additionally, profiling the functions and optimizing the slowest parts of the code can lead to improved performance.

Describe a time you encountered an issue while working with AWS Lambda and how you overcame it.

Recently, while working with AWS Lambda I encountered an issue with a particular function not returning the expected results.
After some investigation, I found that the lambda function had a bug in the logic related to a particular parameter.
To resolve this issue, I implemented the following solution.
First, I started by debugging in my local development environment to confirm my suspicion of a bug.
Once confirmed, I deployed a new version of the function and configured a test environment in AWS Lambda.
This allowed me to run the test cases with different parameters and gain more insight into the cause of the problem.
The solution involved writing a code snippet to replace the parameter with an alternate one that would return the correct result.
This involved examining the existing parameters and making sure that the alternative parameter was both compatible and valid.
In addition, I also needed to make sure that the code snippet was robust in its test of the parameter and would not pass an invalid value to the lambda function.
Once tested and confirmed, I deployed the updated code and tested it in production.
Finally, the issue was resolved and the production environment returned the desired results.
The code snippet I created was as follows:

const parameter = value === 'alternateValue' ? 'correctValue': value;
return parameter;

How comfortable are you with setting up and configuring AWS Lambda functions?

I am comfortable with setting up and configuring AWS Lambda functions.
The process for doing so is relatively straightforward and can be broken down into the following steps.
First, you will need to create an AWS account.
This can be done on the official website.
Second, you will need to create a Lambda function.
This can be done by navigating to the AWS console's Lambda service.
Once there, you can choose the blueprint that best suits your needs, enter the code you wish to use as the function's body, and configure the relevant settings such as memory size, timeout, and more.
Third, you will have to give the function a name, description, and tags, as well as any environment variables which you may require for its execution.
Fourth, you will need to upload the necessary libraries or packages which are required for the function to run correctly.
These can either be included as part of your function code or uploaded to a source such as S3.
Fifth, you will then need to configure the appropriate triggers which will cause the function to execute.
This can be done through Amazon EventBridge, Amazon SNS, or even CloudWatch Events.
Finally, you can test your Lambda function to ensure it works correctly and perform routine maintenance or modifications when needed.
Here is a sample code snippet for creating a Lambda function with Python:

import json
 
def lambda_handler(event, context):
    # Insert logic here
    return {
        'statusCode': 200,
        'body': json.dumps('Hello World!')
    }

Have you ever had to troubleshoot a problem related to AWS Lambda?

Yes, I have troubleshooted problems related to AWS Lambda on a number of occasions.
In order to troubleshoot an issue with AWS Lambda, it is important to first pinpoint the exact problem and then determine the cause of the error.
After doing so, you can then start debugging your Lambda functions.
One of the main approaches is to use the built-in logging and metrics in the AWS environment to try and identify the root cause of the issue.
Additionally, you can try different debugging techniques such as adding console logs or setting breakpoints and stepping through code to identify where the issue lies.
Another helpful approach is to deploy your Lambda function in multiple stages - such as testing, staging, and production - and debug each stage separately.
Finally, if the issue persists, you may need to contact AWS Support for further assistance.
A sample code snippet that may help in debugging an issue with an AWS Lambda function is provided below:

console.log('Check for errors in Lambda function');
try {
  //Your Lambda code here
  log('No errors found');
}
catch(err) {
  console.log('Error found');
  console.error(err);
}

What challenges have you faced while developing applications on top of AWS Lambda?

Developing applications on top of AWS Lambda can present a number of challenges.
Firstly, due to the lack of a file system, saving large amounts of data to a database can be difficult.
Additionally, there may be latency issues when processing certain types of requests or when making API calls.
Additionally, debugging can be difficult due to the limited visibility into application logs.
Finally, the code must be written so that it fits within the Lambda memory and time restrictions.
To mitigate these issues, developers should employ best practices such as optimizing their code for memory and time restrictions and writing smaller functions that are easier to debug.
Additionally, developers should use AWS monitoring tools such as CloudWatch or CloudTrail to identify and troubleshoot potential issues.
To manage larger data sets, using databases like Amazon DynamoDB or AWS S3 buckets can help store the data safely and securely.
Finally, when deploying applications to Lambda, it's important to set up a deployment pipeline with automated tests to ensure the code is functioning correctly and meets security standards.
For example, one can use an AWS CodePipeline to automate the build, test, and deploy process.
The following is an example of a CodeBuild script for setting up a deployment pipeline for Lambda applications.

// CodeBuild script
// Install pre-requisites
  sudo apt-get update
  sudo apt-get install -y python3 python3-pip
  pip3 install --user -r requirements.txt

// Set environment variables
  export AWS_ACCESS_KEY_ID={your_access_key}
  export AWS_SECRET_ACCESS_KEY={your_secret_key}

// Deploy application to Lambda
  aws lambda create-function \
    --function-name myapp \
    --runtime python3.6 \
    --role {IAM role} \
    --handler lambda_function.lambda_handler \
    --zip-file fileb://build/package.zip

// Run automated test suite
  python3 test_suite.py

// Monitor application
  aws lambda add-permission \
    --function-name myapp \
    --action lambda:InvokeFunction \
    --statement-id MonitorMyApp

// Logging
  aws cloudwatch put-metric-alarm \
    --alarm-name MyAppErrors \
    --metric-name Errors \
    --namespace ApplicationErrors \
    --statistic Sum \
    --period 30 \
    --threshold 10 \
    --comparison-operator GreaterThanThreshold \
    --dimensions Name=FunctionName,Value=myapp

How do you ensure that AWS Lambda functions are secure and compliant with company policies?

Ensuring that AWS Lambda functions are secure and compliant with company policies can be done in several ways.
Firstly, you should use network security tools such as firewalls and intrusion detection systems to set up access control lists for the incoming and outgoing traffic.
Additionally, IAM roles should be used to limit access to the Lambda functions and only allow certain users to have permission to deploy or modify the code.
Furthermore, when setting up your Lambda functions, you should take advantage of encryption methods such as KMS and SSL/TLS certificates to ensure that all data is secure during transit.
Finally, you should audit and monitor your functions on a regular basis to ensure that they remain secure and compliant with company policies.
Here is an example of code you can use to enable encryption for your Lambda functions:

const kmsEncryption = {
  'key': 'alias/your-kms-key-here',
  'keySpec': 'AES_256'
};

exports.myHandler = (event, context, callback) => {
  const encryptionParams = {
    'type': 'KMS',
    'kmsEncryption': kmsEncryption
  };

  context.callbackWaitsForEmptyEventLoop = false;
  context.encryptionParams = encryptionParams;
  
  // put other logic here
  
  callback(null, 'Function successfully encrypted!');
};

What strategies do you use to ensure the scalability and reliability of AWS Lambda-based applications?

Building AWS Lambda-based applications with scalability and reliability in mind is not an easy task.
There are various strategies you should consider when building such an application.
First, you should design for scalability from the outset.
This means modularizing your code and using services like containers to spin up replicas of your application when needed.
Additionally, you should design your application to have an asynchronous architecture that can handle events in the background.
This means using queuing services such as Amazon SQS, or leveraging event sources such as Amazon Kinesis Data Streams.
You also need to account for the fact that AWS Lambda functions have a maximum execution duration of 15 minutes.
To ensure reliability, you should use retry mechanisms, circuit breakers, and handle errors gracefully.
Finally, it's important to monitor the performance of your application using services such as Amazon CloudWatch.
You can set up alarms and notifications so that you can respond quickly in case of any issues.
Below you will find a sample code snippet which illustrates how you can use AWS Lambda to increase scalability of your application:

// Create an AWS Lambda function 
const myFunction = (event, context, callback) => {
  // Perform your operations here
  ...
  // Invoke another Lambda function
  const params = {
    FunctionName: 'anotherFunction',
  };
  const lambda = new AWS.Lambda();
lambda.invoke(params, (err, data) => {
    if (err) {console.log(err);} 
    else {console.log(data);}
    callback();
  }); 
};

exports.handler = myFunction;

What challenges do you think developers will face when working with serverless architectures?

Developers working with serverless architectures often face the challenge of optimizing cold start times.
Cold start time is the time it takes for a serverless function to initialize and respond to an incoming request.
This can be difficult as you have to ensure the serverless function is properly resourced with enough memory, CPU, and storage space to handle the workload.
Additionally, due to the ephemeral nature of serverless functions, you must also consider scalability and availability when building and deploying applications with serverless architectures.
One approach to solving this challenge is through the use of containerization technology like Docker or Kubernetes.
By containerizing your serverless functions, you can ensure that each instance of the function has the same environment, allowing for more consistent performance and better optimization for cold start times.
Additionally, by running multiple instances of the same serverless function in isolated containers, you can minimize the impact of any single instance failure.
Another way to tackle the issue of cold start times with serverless architectures is to employ caching strategies.
Caching allow developers to temporarily store and quickly retrieve data, helping reduce response times for repetitive tasks.
For example, using a caching service like Redis, developers can store program state and user data in the cloud for faster access and retrieval.
To illustrate this, here is a code snippet demonstrating how to implement caching using Redis:

```
// Connect to Redis cache
var redis = require("redis");
var client = redis.createClient(<port>, <host>);

// Store data in Redis
client.set("userData", JSON.stringify(data));

// Get data from Redis
client.get("userData", (err, result) => {
    if (err) throw err;
    
    let cachedData = JSON.parse(result);
});  
```

By implementing caching strategies, developers can significantly reduce cold start times and provide better performance for their applications.

Do you have any experience deploying AWS Lambda functions to production environments?

Yes, I do have experience deploying AWS Lambda functions to production environments.
The process is relatively straightforward and doesn't require much coding, as most of the work can be done within the AWS Lambda console.
First, you will need to create a new function in the Lambda console and assign it an execution role that allows it to access AWS services.
This can be done by selecting the "Create a new IAM role with basic Lambda permissions" option when creating the function.
Next, you will need to add any required configuration settings and libraries for your function.
You can either use a zip file or upload the code directly into the editor.
To deploy your function, you will need to set an appropriate memory size and timeout duration, and then click on the "Deploy" button.
Finally, you will need to configure the API Gateway to access the functions.
This can be done by choosing the "API Gateway" option when creating the function, and then following the steps outlined in the console.
Once all the steps have been completed, your function should be ready to handle incoming requests.
To test it out, simply add a "test" event to the Function page, and then click on the "Test" button.
If the function is working correctly, you should see the response in the "Execution Result" section.
Here is a code snippet that shows how to deploy the Lambda function:

const AWS = require('aws-sdk');
const lambda = new AWS.Lambda({region: '<region>'});

exports.handler = async (event) => {
   const params = {
      FunctionName: '<function_name>', // Your Lambda Function name
      Publish: true
   };

   // Deploy the function
   await lambda.updateFunctionConfiguration(params).promise();
};

In what ways have you used AWS Lambda to optimize cost?

Using AWS Lambda can help you optimize cost in a variety of ways.
Most importantly, using Lambda means that you can eliminate the need to pay for idle resources, as the service is autoscaleable and will only run code when needed.
This results in much lower costs compared to running a traditional server.
Additionally, Lambda's per-request pricing model can be beneficial to cost optimization, as you can pay for usage rather than an up-front fee for a resource that might not be used often or optimally.
To make use of Lambda as an effective cost optimization tool, it is important to understand what kind of workloads are best suited for the service.
Many workloads that are event-driven, short-lived, stateless, and parallelizable are ideal for execution with Lambda.
Additionally, it is important to take advantage of the ability to trigger a Lambda function in response to an event from any supported Amazon Web Services (AWS) service, as this can reduce the amount of time spent managing resources outside of the AWS environment.
To implement an optimized Lambda solution, here is an example of code written in Python:

import json

def lambda_handler(event, context):
    # processing logic here...

    return {
        "statusCode": 200,
        "body": json.dumps('Lambda cost optimization successful!')
    }