In this blog post, we are going to guide you through the top AWS Lambda Interview Questions and their answers in detail, so that you can confidently explore AWS Lambda and its usability properly.
So before going started with the AWS Lambda Interview Questions and their answers first let’s understand what it is and why we need it.
Aws is Amazon cloud services name as Amazon Web Services which is used for cloud computing, cloud storage and server or serverless computing and many more cloud functionally.
Aws contain different cloud services for specific operations like AWS S3, AWS EC2, AWS Lambda, AWS Redshift and so on with unique ways to handle, manage and process the data.
We can a discuss lot more about AWS here but our focus is to learn about AWS Lambda Interview Questions so we will start with that for more AWS functionality Read Here
Top 30 AWS Lambda Interview Question and Answer:
1. What is AWS Lambda?
If you’re wondering what AWS Lambda is, you’re not alone. This relatively new AWS service has been gaining a lot of popularity lately, but it can still be a bit of a mystery to some. So, let’s clear things up.
Simply put, AWS Lambda is a way to run code without having to provision or manage servers. You upload your code to AWS Lambda, and the service runs it for you. It’s that simple, Of course, there’s a bit more to it than that.
AWS Lambda is a “serverless” compute service, which means that you can execute code without having to worry about spinning up or managing any servers. This is a major advantage, as it greatly reduces both the cost and complexity of running code.
AWS Lambda is also event-driven, meaning that it can automatically run code in response to events from other AWS services.
For example, you could use AWS Lambda to automatically run code when a file is uploaded to S3, or an event is fired in DynamoDB.
Overall, AWS Lambda is a powerful and easy-to-use service that can be a great addition to your toolkit. If you’re looking for a way to run code without having to provision or manage servers, or you need an event-driven solution, AWS Lambda is definitely worth checking out.
Related Article: How to Start your Career in AWS? – Complete Guide
2. What are the Benefits of using AWS Lambda?
There are many benefits to using AWS Lambda, including the fact that it can save you money on your overall AWS bill.
Lambda is a serverless computing service which is very significant in many ways that allow you to run code without any provisioning or managing servers.
This means that you can save time and resources when you need to run code for your applications.
In addition, Lambda can automatically scale your application code according to the amount of traffic that your application is receiving.
This means that your application will be able to handle more traffic without needing to provide additional resources.
Finally, Lambda is a great way to increase the overall performance of your applications. Lambda functions can execute code quickly and efficiently, which can free up resources for other parts of your application.
Related Article: What is AWS Lambda Function? – Ultimate Guide
3. When should I use Lambda?
If you’re considering using AWS Lambda for your next project, you might be wondering when is the right time to use this service. Here are a few scenarios where AWS Lambda can be a great fit:
So, when should you use AWS Lambda? If you need a scalable, cost-effective, serverless solution, then AWS Lambda is a great option to consider.
Lambda is a great tool for many different situations.
A. If you need a Scalable Compute Solution:
AWS Lambda can automatically scale up or down based on demand, so you don’t have to worry about provisioning or managing servers.
B. If you need a Cost-Effective solution:
Because Lambda only charges you for the actual time your code is running, it can be a very cost-effective solution, especially compared to traditional server-based solutions.
B. If you need a Serverless Solution:
With Lambda, there is no need to provision or manage any servers, making it a truly serverless solution. This can be a great benefit in terms of both simplicity and cost.
If you need to process data in real time, Lambda is a good choice. It can automatically run your code when events occur, making it very convenient.
Lambda is also a good choice if you need to scale your code quickly and easily. It can automatically provision and manage resources, so you don’t have to worry about it.
If you’re not sure whether Lambda is right for you, try it out and see how it goes. It’s easy to get started and you can always switch to another solution if it doesn’t work out.
4. What are the AWS Lambda Features?
AWS Lambda is a computing service that allows running code without provisioning or managing servers means serverless computing is used to build serverless applications and APIs.
Some of the key features of AWS Lambda are:
- Lambda functions can be written in any of the supported languages: Node.js, Java, Python, and C#.
- Lambda functions can be triggered by events from Amazon S3, Amazon DynamoDB, Amazon Kinesis, and Amazon Simple Notification Service (SNS).
- Lambda automatically scales your code based on the number of requests.
- Lambda charges you only for the time your code is executed and no charge for the ideal state.
5. How to get started with AWS Lambda?
Now that you understand some of the benefits of using Lambda, let’s take a look at how you can create and use a Lambda function.
Creating a Lambda Function:
Creating a Lambda function is a quick and easy process, In this section, we will show you how to create a function and use it in a web application.
First, you will need to create a Lambda function. You can do this by clicking on the “Create a Function” button in the Lambda dashboard.
Next, you will need to provide a name for your function and select a programming language. For this example, we will use Python.
The next step is to select a blueprint. A blueprint is a template that defines the functionality of your Lambda function. For this example, we will use the “Hello World” blueprint.
The final step is to provide the function code. This is the code that will be executed when your Lambda function is called. For this example, we will simply print “Hello World!”
def hello_world(event, context):
print(“Hello World!”)
Now that your function is created, you can test it by clicking on the “Test” button.
Using a Lambda Function in a Web Application:
Now that you have created a Lambda function, you can use it in a web application. In this example,
we will show you how to use a Lambda function to process a form submission.
First, you will need to create a form. This form will contain the following fields:
Name Email Message
The following code will process the form submission and send the email message.
def send_email(event, context):
# Extract the name, email, and message from the event.
name = event["name"]
email = event["email"]
message = event["
try:
# Send the email.
mail_server = smtplib.SMTP()
mail_server.connect(mail_server_address)
mail_server.login(mail_server_username, mail_server_password)
message = mail_server.sendmail(email, ["recipient@domain.com"], message)
mail_server.quit()
except:
# Log the error.
print("Error sending email: " + str(error))
6. How to use AWS Lambda for your Business Needs?
Lambda can be used for a variety of applications, including web and mobile applications, backend services, and IoT applications.
Lambda is a great choice for businesses that need to run code in a cost-effective and scalable way. In this blog post, we’ll show you how to use Lambda for your business needs.
If you’re not familiar with Lambda, we recommend checking out the AWS Lambda documentation first. After you’ve familiarized yourself with Lambda, you can start using Lambda for your business needs.
To use Lambda for your business needs, you first need to create a Lambda function. A Lambda function is a piece of code that is run in response to an event.
You can create a Lambda function using the AWS Lambda console, the AWS Lambda API, or the AWS Command Line Interface (CLI).
Once you’ve created your Lambda function, you need to configure it. You can configure your Lambda function using the AWS Lambda console, the AWS Lambda API, or the AWS Command Line Interface (CLI).
After you’ve created and configured your Lambda function, you can test it to make sure it works as expected.
Once you’ve tested your Lambda function, you can deploy it to production.
Lambda is a great choice for businesses that need to run code in a cost-effective and scalable way, If you’re not familiar with Lambda, we recommend checking out the AWS Lambda documentation first.
After you’ve familiarized yourself with Lambda, you can start using Lambda for your business needs.
7. What are some of the best practices for using AWS Lambda?
If you’re looking to get the most out of AWS Lambda, there are a few best practices you should keep in mind. First and foremost, Lambda functions should be stateless.
This means that they should not rely on any data that is not passed in through their input parameters.
Another important best practice is to design your Lambda functions for high availability, This means making sure that your function can handle any spikes in traffic or other unexpected events.
Finally, you should keep your Lambda functions as small as possible. This will help keep your costs down and make your functions more responsive.
By following these best practices, you can maximize the benefits of using AWS Lambda.
8. What are some of the Challenges of using AWS Lambda?
If you’re considering using AWS Lambda, you may be wondering what some of the challenges of using this service are.
1. Cold starts.
In the lambda starting phase, When you first invoke a Lambda function, there may be a slight delay while the function initializes, This is known as a “cold start” and can be a bit frustrating if you’re expecting your function to be super speedy.
2. Vendor lock-in.
As AWS Lambda is a proprietary service from Amazon, you may be concerned about being locked into their platform. While it’s true that you’ll need to use AWS to run your Lambda functions, you can still take advantage of other services and hosting providers for the rest of your architecture.
3. Limited language support.
Currently, AWS Lambda only supports a few languages for writing your functions, including Node.js, Java, and Python. If you’re using a different language, you’ll need to find a workaround.
4. Function size limit.
There is a limit to how large your Lambda functions can be, which may be a problem if you’re dealing with large amounts of data.
5. Concurrency limit.
If you’re expecting a lot of traffic to your Lambda function, you may need to be aware of the concurrency limit. This is the maximum number of concurrent executions that can happen at one time.
Despite these challenges, AWS Lambda can be a great solution for many use cases. Just be sure to keep these things in mind before you get started.
9. How to use AWS Lambda in your Application Stack?
If you’re looking to use AWS Lambda in your application stack, there are a few things you need to know. In this blog post, we’ll cover how to get started with using AWS Lambda and how it can benefit your application.
Lambda can be used for a variety of applications, including back-end services, event-driven applications, and data processing jobs.
Getting started with Lambda is easy. All you need is an AWS account and to install the AWS CLI. Once you have the CLI installed, you can create a Lambda function using the “create-function” command.
Lambda functions can be written in a variety of languages, including Node.js, Java, and Python. In this blog post, we’ll use Node.js as our example language.
Once you have a Lambda function created, you can invoke it using the “invoke” command. Lambda will then run your code and return the results.
Lambda is a great way to run code without having to provision or manage servers. It’s also easy to get started with and can be used for a variety of applications. If you’re looking to use AWS Lambda in your application stack, give it a try today.
10. How to Troubleshoot AWS Lambda Functions?
If you’re having trouble with your AWS Lambda functions, there are a few things you can do to troubleshoot the issue.
First, check the Lambda function’s logs to see if there are any error messages. You can view the logs in the AWS Lambda console.
Next, check the function’s configuration to make sure everything is set up correctly. Make sure the function has the correct permissions and triggers.
If the problem persists, contact AWS support for help.
11. How you can Access Lambda Function in AWS?
If you want to access a Lambda function in AWS, you can do so using the AWS Lambda console, the AWS Lambda API, or the AWS Command Line Interface (CLI).
To access a Lambda function using the AWS Lambda console, simply sign in to your AWS account and navigate to the Lambda service.
From there, you will see a list of all the Lambda functions you have created. Simply click on the function you want to access and you will be taken to the function’s page.
To access a Lambda function using the AWS Lambda API, you will need to use an HTTP client such as cURL.
The API endpoint for Lambda is lambda.amazonaws.com. To access a specific function, you will need to use the following format:
https://lambda.amazonaws.com/2015-03-31/functions/{function_name}/invocations
To access a Lambda function using the AWS CLI, you will first need to install the AWS CLI. Once you have done so, you can access Lambda functions using the following command:
aws lambda invoke --function-name {function_name} --invocation-type RequestResponse --log-type Tail --payload '{"key1":"value1","key2":"value2","key3":"value3"}' outputfile.txt
This will invoke the Lambda function with the name you specified and write the function’s output to the file outputfile.txt.
To understand accessing the AWS Lambda using CLI use can read this document
12. How to Clean up or delete AWS Lambda Function?
If you want to clean up or delete an AWS Lambda function, there are a few things you need to do. First, you need to delete the function from the Lambda console.
Second, you need to delete the function’s resources, including any event source mappings, logs, and alarms.
Finally, you need to remove the function’s IAM role.
To delete a Lambda function from the Lambda console:
1. In the Lambda console, select the function you want to delete.
2. In the function’s details pane, click Delete.
3. In the confirmation dialog, click Yes, Delete.
To delete resources associated with a Lambda function:
1. In the Lambda console, select the function you want to delete.
2. In the function’s details pane, click Delete Resources.
3. In the confirmation dialog, click Yes, Delete.
To delete an IAM role associated with a Lambda function:
1. In the IAM console, select the role you want to delete.
2. In the role’s details pane, click Delete.
3. In the confirmation dialogue, click Yes, Delete.
- What are the languages supported by AWS Lambda?
AWS Lambda is a serverless computing platform good for running code directly without doing any server setup or managing servers. Lambda supports a variety of languages, but main languages like Node.js, Java, Python, and C#. it supports PowerShell, Ruby, and Go also.
13. Anonymous Class Vs Lambda Function: What is the difference?
There are two ways to create an anonymous class in Java: using an anonymous inner class or using a Lambda function.
Anonymous inner classes are created by extending a class or implementing an interface, without giving the class a name.
They are often used when you need to override a method from a superclass or interface. Lambda functions, on the other hand, are nameless functions that can be passed around as arguments.
They are usually used when you need to perform a simple operation, such as a mathematical function, without defining a named class or method.
So, what is the difference between an anonymous class and the Lambda function? Let’s take a closer look.
15. Anonymous Class Vs Lambda Function: Advantages and Disadvantages
Advantages of Anonymous Inner Classes:
1. You can override methods from a superclass or interface.
2. They are easier to read than Lambda functions.
3. They can access instance variables of the enclosing class.
Disadvantages of Anonymous Inner Classes:
1. They require more code than Lambda functions.
2. They can only be used in one place.
3. They can only extend one class or implement one interface.
Advantages of Lambda Functions:
1. They are more concise than anonymous inner classes.
2. They can be used in multiple places.
3. They can be assigned to variables.
4. They can be passed as arguments to methods.
5. They can be returned from methods.
Disadvantages of Lambda Functions:
1. They cannot access instance variables of the enclosing
16. What is a Serverless App in AWS Lambda?
A Serverless App in AWS Lambda is an app that is completely run by AWS Lambda. There is no need for a server to run the app, making it extremely scalable and cost-effective.
Lambda handles all the administration of the underlying compute resources, making it an ideal platform for developers who want to build applications that are highly scalable and cost-effective.
Lambda is a perfect fit for a Serverless App in AWS because it automatically scales up or down based on the number of requests.
This means that your app can start small and scale up as needed, without having to provision or manage any servers.
In addition, Lambda is very cost-effective, You only pay for the compute resources that your code uses, and no cost for not running Code. This makes Lambda an ideal platform for developing and deploying Serverless Apps.
17. What is Serverless Application on AWS Lambda?
A serverless application is an application that is built using cloud services such as Amazon Web Services (AWS) Lambda.
A serverless application does not require a server to run, and is typically deployed using a cloud provider such as AWS.
In a traditional application, the application is deployed on a server, and the server is responsible for running the application.
In a serverless application, the application is deployed on a cloud provider such as AWS Lambda, and the cloud provider is responsible for running the application.
The benefits of using a serverless application are that you do not need to worry about provisioning or managing servers, and you can use a pay-as-you-go model where you only pay for the resources that you use.
18. How to Get started with the Serverless Application on Lambda?
To get started with developing a serverless application on AWS Lambda, you will need to create an AWS account and create a Lambda function.
Creating an AWS account is free, and you can create a Lambda function by going to the AWS Lambda console and clicking the “Create Function” button.
When creating your Lambda function, you will need to specify a name and description, and you will need to choose a runtime. For this example, we will use the Node.js runtime.
Once you have created your Lambda function, you can code your application logic in the Lambda console. For this example, we will write a simple function that takes an input string and outputs the string in all uppercase.
After you have coded your application logic, you can deploy your serverless application by clicking the “Deploy” button in the Lambda console.
When you deploy your application, you will need to provide a function name and an invocation URL. The invocation URL is the URL that will be used to execute your Lambda function.
You can also specify an environment variable named “ACCESS_KEY” that will contain your AWS access key. This environment variable is optional, but if you do not specify it, you will need to provide your AWS access key when you invoke your Lambda function.
Once you have deployed your serverless application, you can test it by going to the invocation URL in a web browser.
19. What is AMI in Lambda?
An Amazon Machine Image (AMI) is a special type of virtual appliance that is used to create a virtual machine inside the EC2 (Amazon Elastic Compute Cloud).
An AMI includes a template for the root volume for the instance (such as an operating system, an application server, and applications), launch permissions that control which AWS accounts can use the AMI to launch instances.
Similarly AWS accounts can use the AMI to block device mapping that specifies the volumes to attach to the instance while launched.
20. How to build an AMI in AWS Lambda?
Each AMI contains a complete operating system and pre-installed software which makes it possible to launch a virtual machine of a given type.
Since AWS Lambda is a serverless platform, it does not provide any support for creating and using the AMIs, However, there is a workaround that can be used to create an AMI in AWS Lambda.
The first step is to create a Lambda function and configure it to use an Amazon S3 bucket as its event source.
The next step is to create an Amazon EC2 instance and configure it to use the Lambda function as its event source.
Finally, you can launch the EC2 instance and create an AMI from it.
Keep in mind that this workaround will only work if the Lambda function is configured to use an Amazon S3 bucket as its event source.
If you try to use a different event source, such as an Amazon DynamoDB table, the Lambda function will not be able to trigger the EC2 instance and create the AMI.
21. What are the Security Measures in AWS Lambda?
Security is always a top priority when it comes to cloud computing, and AWS Lambda is no exception. In this blog post, we’ll take a look at some of the security measures in place for AWS Lambda.
First and foremost, AWS Lambda runs in a secure, isolated environment called a “VPC”. This means that your Lambda functions are isolated from the rest of the internet, and can only communicate with other resources inside the VPC (such as an RDS database).
In addition, Lambda functions can only access resources that have been explicitly granted access. By default, Lambda functions have no access to any other AWS resources.
To grant a Lambda function access to another resource, such as an S3 bucket, you’ll need to create an IAM role and attach it to the Lambda function.
Finally, AWS Lambda uses encryption to protect your code and data while it is in transit. All Lambda functions are encrypted with a unique customer-specific key, and data is encrypted at rest.
These are just a few of the security measures in place for AWS Lambda. For more information, be sure to check out the AWS Lambda documentation.
22. What is Elastic Blockage Storage in AWS Lambda?
If you’re like most people, you probably think of AWS Lambda as a compute service, But Lambda is also a storage service and a pretty powerful one at that.
Lambda’s storage offering is called Elastic Block Store (EBS). EBS is a block-level storage volume that you can attach to a running Lambda function. Once attached, you can read and write data to the volume just like you would with any other block device.
The big advantage of using EBS with Lambda is that you can persist data across invocations of your function. This is useful for a number of reasons.
For example, you can use EBS to cache data that your function needs to access frequently. Or you can use it to store data that your function generates so that it can be processed later.
EBS is also a great way to share data between Lambda functions. If you have multiple Lambda functions that need to access the same data, you can attach an EBS volume to one of the functions and then mount it in the other functions.
This gives you a convenient way to share data between Lambda functions without having to worry about managing the underlying storage infrastructure.
If you’re using Lambda, you should definitely check out EBS, It’s a simple and convenient way to add storage to your Lambda functions.
23. What is Scaling in AWS Lambda?
AWS Lambda is very good at scaling, You can use AWS Lambda to run code for virtually any type of application or backend service all with zero administration.
Lambda automatically scales your application by running code in response to each trigger and No need to provision or manage any servers.
Each time you save your function code, Lambda takes care of compiling and packaging it, and uploading it to AWS Lambda.
When Lambda receives a trigger, it first uses a compute resource that is allocated to your function. These resources are defined by the amount of memory you allocate to your function.
For example, if you allocate 512 MB of memory to your function, Lambda will use 1 vCPU.
As your application grows and more events trigger your function, Lambda dynamically scales out by adding more compute resources.
There is no limit to the number of resources that can be added, and you only pay for the compute time you consume.
Lambda uses a container-based model to run your code, This means that your code runs in an isolated environment, making it easy to package and deploy.
Lambda also provides built-in security, so you can trust that your code will run in a secure and reliable environment.
With Lambda, you can focus on building code, without worrying about the underlying infrastructure. Lambda automatically scales your code to meet demand, so you can worry less about capacity planning.
And since Lambda is a fully managed service, there is no need to patch or monitor servers.
24. What is Serverless Computing?
Serverless computing is a new type of cloud computing that allows you to build and run applications without having to provision or manage any servers.
Serverless computing takes away the need to worry about server maintenance, capacity planning, or patching. You can simply write your code and deploy it without having to worry about any of the infrastructures.
Serverless computing is a great option for many applications, especially those that are event-driven or have unpredictable workloads.
25. What are Vertical Scaling and Auto Scaling in AWS Lambda?
If you’re using AWS Lambda, you may be wondering what vertical scaling and Auto Scaling are, Both are important concepts to understand in order to get the most out of your Lambda functions.
Vertical scaling is the process of increasing the computing power of a single Lambda function, This can be done by increasing the amount of memory that the function has access to, or by changing the function’s CPU setting.
Increasing the memory size will generally make the function run faster while changing the CPU setting will change how much processing power the function has.
Auto Scaling is the process of automatically scaling Lambda functions in response to changes in demand, This can be done by using AWS Lambda’s built-in Auto Scaling feature, or by using a third-party tool like AWS OpsWorks.
Auto Scaling allows you to scale your Lambda functions up or down automatically, based on criteria that you define, This can be a great way to ensure that your functions are always able to meet the demands of your application.
Both vertical scaling and Auto Scaling are important concepts to understand when using AWS Lambda, By understanding how these concepts work, you can ensure that your Lambda functions are always performant and available when you need them.
26. What is the Lambda Expression?
Lambda expressions are one of the most powerful features in programming like java or Python, They allow you to succinctly express behaviour in your code, without having to write a lot of boilerplate code.
Lambda expressions are a way to represent a behaviour or a function in your code, They are typically used when you want to pass behaviour as an argument to another method, or when you want to create an anonymous class that implements a specific interface.
Lambda expressions are written using the following syntax:
(argument list) -> {body}
Lambda expressions can be extremely powerful and can help you write more concise and readable code.
27. How VPC networking for Lambda works?
If you’re using AWS Lambda in a VPC, you need to configure your Lambda function and the VPC subnets to work together.
This can be a bit of a challenge, but once you get it set up, it’s actually quite straightforward. Here’s a quick rundown of how VPC networking for Lambda works.
When you create a Lambda function, you specify the VPC that you want it to run in, Lambda will then create an elastic network interface (ENI) in that VPC, This ENI will have an IP address from one of the subnets in the VPC.
The Lambda function will then use this ENI to communicate with any resources in the VPC, such as an RDS database or an S3 bucket.
The ENI will also be used to communicate with any other AWS services that are not in a VPC, such as DynamoDB or SNS.
One thing to keep in mind is that each Lambda function can only have one ENI. This means that if you have multiple Lambda functions in a VPC, they will all share the same ENI. This can be a bit of a bottleneck, so it’s important to plan accordingly.
Another thing to keep in mind is that Lambda functions can only access resources in the VPC that they are running in. This means that if you want to access resources in another VPC, you’ll need to set up a VPN or some other type of connection.
That’s a quick overview of how VPC networking for Lambda works. As you can see, it’s not too complicated. But there are a few things to keep in mind
28. How do Lambda Deployment Packages Work?
Lambda functions are deployed as packages. A typical Lambda function deployment package includes the function code, associated dependencies, and any other resources needed to run the function.
When you create a Lambda function, you specify the code and any dependencies required by your function. AWS Lambda uses this information to create a deployment package that contains your function code and dependencies.
Once the package is created, it is uploaded to an Amazon S3 bucket. You can then specify the Amazon S3 bucket location when you create or update your Lambda function.
AWS Lambda will automatically update your function code when it detects a new version in the Amazon S3 bucket. This makes it easy to deploy and manage Lambda functions.
29. What is the Lambda Execution Environment?
Ever wondered how AWS Lambda manages to provide such a speedy and reliable service? Well, part of the answer lies in the way the Lambda execution environment is designed.
The Lambda execution environment is based on Amazon Linux, which is a lightweight version of Red Hat Enterprise Linux (RHEL). Amazon Linux is designed to provide a stable, secure, and high-performance environment for Lambda functions.
Lambda uses an isolated environment for each function invocation. This means that each invocation gets its own copy of all the resources it needs, including the Amazon Linux kernel.
This isolation makes it much easier to manage and update Lambda functions, as there is no need to worry about dependencies and compatibility issues.
Lambda also uses a read-only file system for each invocation. This file system is used to store the function code and any additional libraries that the function may need.
This file system is completely separate from the Amazon Linux kernel, so there is no need to worry about file system permissions.
The Lambda execution environment is designed to be highly reliable. It uses multiple Availability Zones (AZs) to provide high availability and fault tolerance. Lambda will automatically detect and recover from failures in individual AZs.
Lambda is also designed to be scalable. It can automatically scale up or down based on the invocation rate. This means that Lambda can handle bursts of traffic without missing a beat.
All of this together makes Lambda a powerful and flexible platform for running code in the cloud.
30. What is Lambda Reserved Concurrency?
Lambda reserved concurrency is a new feature that allows you to improve the performance and scalability of your Lambda functions.
It allows you to reserve a certain amount of concurrency for your function so that it can scale more smoothly and handle more traffic.
Conclusion
I hope you have got the idea about AWS Lambda and why you nee it, this different type of AWS Lambda Interview Questions and answers can help you to prepare for AWS Interview.
This type of question mostly ask in the Data Engineer profile interview of cloud architect interview, similarly if want to pursue a career in cloud computing or Data Engineering then AWS is the Crusial one.
Meet Nitin, a seasoned professional in the field of data engineering. With a Post Graduation in Data Science and Analytics, Nitin is a key contributor to the healthcare sector, specializing in data analysis, machine learning, AI, blockchain, and various data-related tools and technologies. As the Co-founder and editor of analyticslearn.com, Nitin brings a wealth of knowledge and experience to the realm of analytics. Join us in exploring the exciting intersection of healthcare and data science with Nitin as your guide.
