Rohit Sharma

Could you help me understand more about your background by giving a brief introduction of yourself? So I'm a 2019, uh, BTech IT graduate from, uh, DIT University, Dehradun, where I've completed my bachelor's degree in, uh, IT profession. And after that, I joined them as a internship in the company called Embibe, uh, which is an EdTech platform. And, uh, that over the period of time, I got into a full time environment and, uh, I've been there since then, where I learned a lot about the professional software development, developing web applications. Mostly using Python. And, um, and, yeah, I've I've developed some utility tools for internal teams that help help with their productivity and more efficient workflows. I've contributed along with senior developers, for their existing projects and contributed as a professional Python developer and and web applications. I have an experience of, uh, more than four and a half years. And over this period of time, I was over this period of time, I was I was, uh, sorry. I was I was, uh, I was introduced with technologies and web frameworks like, uh, and databases like, uh, Flask web framework, FastAPI web framework, and Django web framework. These are all Python web frameworks. And the docker, the authorization of Python applications, uh, databases like MongoDB, uh, MySQLDB, Postgres, uh, SQLDB, and caching facilities like that is, uh, monitoring tools like, like, logging tools like, uh, ELK stack or, uh, Loggly. In my recent projects, uh, there were these 5 machine learning services that I had to dockerize and deploy on AWS. So I did that. And along with the authorization, I uh, I integrated I integrated logging framework for those applications. Like, I created a common logging package and then integrated that into all the applications. And it uses, uh, ELK stat. Like, it it it sends logs to, uh, Logstash. And from there, it it it gets forwarded to Elasticsearch, and we can we can see the logs in the Kibana UI. That's like my recent project.

What automated tools would you recommend for continuous integration and deployment in a back-end development pipeline that uses Python and AWS services? What automated tools would you recommend for continuous integration and deployment? I would recommend workflows like GitHub workflow or Bitbucket workflow. If you don't have the access to GitHub workflows, I would recommend hosting a private Jenkin server under your VPN or something and using that to integrate with the building and deployment of your Python applications to AWS infrastructures, which is your AWS ECR, Elastic Container Registry, where you upload your latest Docker build images and ECS, Elastic Container Service, where you can just create new task definitions with your latest image tags and redeploy the service. There's also things like Terraform that you can use instead of Jenkins. That would just make it easier to migrate on some different infrastructure later on. If you wish to migrate to Azure, let's say, then all you need to do is just a Terraform file for Azure for your services and that's it. So continuous integration and deployment in a back-end development pipeline that uses Python and AWS, most standard and most widely used is Jenkins. So I would recommend Jenkins.

What's a common strategy to prevent SQL injection when writing back-end code with Python that interacts with a MySQL database? I would suggest using ORMs like SQLAlchemy and also I'll suggest not to write the complete queries in your code instead you can use placeholders for important variables and you can validate the placeholders before committing the queries. So common strategy is avoid using complete hard-coded SQL queries and instead use pre-validated placeholders and of course try to use ORM like SQLAlchemy to generate your queries. I think that should be it for this question.

what mechanisms would you implement to ensure fault tolerance and high availability of REST API built with Node.js and deploy deployed on AWS mechanisms to ensure fault tolerance and high availability okay so in that case I would do I think we can configure the load balancer for we can we can basically consider the scaling thresholds and scale the application horizontally for high availability yeah I think we can scale the application horizontally for high availability and fault tolerance mechanisms I think AWS provides monitorings on the on the infrastructure metrics so we can define metrics like CPU or memory usage thresholds based on which your application would scale up or scale down automatically so we just need to fine-tune that configuration with respect to our application and our user base for fault tolerance I can also in high availability I think implementing a CDN would also be a very good idea since the applications would be deployed like the application requests would be cached to the nearby geolocations of the of the CDN servers giving low latency to the to the end user so and of course there's caching edge caching so that would really give you high availability in fault tolerance so implementing a CDN would be a good choice for this for this requirement

In what ways do you In what ways do you manage session state persistence in a serverless architecture deployed on AWS Lambda using Python and Node. Js? In what ways do you manage session state persistent persistence in a serverless architecture deployed on AWS Lambda using Python in Node. Js? I think I haven't explored this domain yet, so I'm not sure how how to manage session state persistence and serverless architectures. I have personally or professionally never tried using a serverless architecture for deploying Python or Node. Js applications on AWS Lambda. So I think I'll pass this question for now. But, yeah, I'll surely find out how to do that.

How do you apply the single responsibility principle of SOLID in Python module design considering REST API implementation? So single responsibility principle simply states that a part of code should be only responsible for a single responsibility. So we divide our modules based on that thin layer and we separate each module as per their respective responsibilities. Considering REST API implementation, so I would keep my views separately, I would keep my models in a separate module, I would keep any third party integrations in a separate module. So based on these logical categorization, I would arrange or structure my code base. What else can we do? I would try to use managers with, you know, it depends what design pattern to use, but with managers, I mostly like to use the adapter pattern where you can just, you know, make the manager in such a way that it can adapt to different kinds of underlying technologies if required. So introducing managers that basically takes care of the requests coming from the API views and processing that request using internal modules and internal data files or databases or anything, right? So, yeah, I think that's it for this question.

Look at the JavaScript function that is meant to return the sum of an array of numbers, what logic error is present, and how would you fix it? Function calculate sum. Let's Logic error. What is the logic error, Shiv? I think the logic error is, uh, the limit of the of the for loop. So it says it goes from I less than equal to, uh, length of the array, whereas in in arrays, the index starts from 0, and the last index is, uh, the length of minus 1. So instead of I less than equal to arr.length, it should be I less than, uh, arr.length, like, uh, uh, strict less than. So I think, yeah, that would fix it.

examine this Python code snippet used for initializing a flask application can you spot and explain the flaw and how the application context is being handled from flask import flask app equal to flask name with app dot app context initialize extensions def hello return hello world app dot add URL rule slash will close the application context as soon as you're out of it so spot and explain the flaw and how the application context is being handled so when statement is being used in app dot app context initialize extensions then get out of the context I think extensions should be add URL rule is out of application context it will still work I think I need some need to check some documentation again maybe look on some resources on the internet and then I can solve this one

How would you handle database schema migrations in a continuous delivery environment for Python based applications using postgres SQL. Python based applications, so if you are using Flask, you can I think we can use Flask migrate to run the database migrations? And handle the migration in the docker build When you build a fresh image with the migration, it'll also run the migrations, making sure the, uh, uh, corresponding database in that environment is updated before the application is deployed with the updated code. So in that way, I think we can handle migrations, uh, in a continuous delivery environment. So we just use Flask migrate for migrations if we are using Flask framework. Otherwise, we can find out, like, other migration. Uh, I think SQL Alchemy by default provides this functionality. So yeah. So we integrate the migration and the docker build before building the new application.

What is your method for securing REST API endpoints when building Python applications? It is good to have a question and answer this question one more time, okay? Good to have a question, okay. Method for securing REST API endpoints when building Python applications, the most easiest way is to just handle the security at the API gateway layer, otherwise, if you want to handle the security inside the application, then first you need to decide what security basically you want to use. If it's like a basic user password authentication, like a basic auth, or is it like a Java web token, Java web token based authentication, or like a API key or a license key based authentication. So you can implement any one that you would want for your particular use case. So yeah, there are many ways. What is my method for securing REST APIs? I think verifying the JWT token is a good method if you're using JWT tokens, otherwise, an API key is a good option, like you share the API keys with your targeted users privately, but still there are like chances of leakages, yeah.

How would you monitor and debug performance issues in a Python application interfacing with PostgreSQL on AWS? Monitor and debug performance issues, so AWS provides you these infrastructure metrics that you can see and you can find out if your infrastructure is okay, like your CPU usage and your memory usage and Other things networking and other things also you can go through the logs in the in the in the log section and you can see your application server logs and Try to debug from there otherwise Otherwise you can you can just otherwise you can just go to your personal login setup and maybe access those logs and see if you are able to find some insights from there if if not, I would what I would do is I would just try to run the application locally and Try to reproduce the issue and yeah, that would that would significantly Give me an insight on whether where the issue lies basically