Confluent Senior Software Engineer Interview Questions and Answers

Codility coding rounds , where code execution is mandatory and system design qiestions

Q1. Again execute code and technical questions
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Q1. One more coding test and tech questions
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Q1. Design patterns
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Q2. Design Patterns , scenario based
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Q1. Word formation Algorithm question
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Q2. Architecture of project.
Ans. 
The architecture of the project is based on microservices with a containerized approach using Docker and Kubernetes.
• Utilizes microservices architecture for scalability and flexibility

• Containerized approach using Docker for easy deployment and management

• Orchestrated with Kubernetes for automated scaling and monitoring
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4 questions in 1 hour 30 minutes

Q1. Subset sum - in leetcode
Ans. 
Subset sum problem is a classic algorithmic problem in computer science.
• The problem asks to find if there is a subset of the given array that sums up to a given target value.

• It can be solved using dynamic programming or backtracking.

• The time complexity of the dynamic programming solution is O(n*target), where n is the size of the array.

• The problem can be further optimized using memoization or by using a boolean array t
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Q1. What would you use to build a web app?
Ans. 
To build a web app, I would use a combination of front-end technologies like HTML, CSS, and JavaScript, along with back-end technologies like Node.js, Express, and MongoDB.
• Front-end technologies: HTML, CSS, JavaScript

• Back-end technologies: Node.js, Express, MongoDB

• Frameworks: React, Angular, Vue.js

• Database: MySQL, PostgreSQL, MongoDB

• Version control: Git

• Deployment: AWS, Heroku, Netlify
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Coding - 1 hr, Graph

Coding - 1hr, Algo + Implementation, Bitwise, Prefix search, Intervals.

Coding - 1 hr, Implementation, Testing.

Q1. Details around projects.
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Q2. Standard Behavioural Questions.
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Coding - 1hr, Systems Design with coding, Multithreading.

Round duration - 60 minutes
Round difficulty - Medium

4 coding questions on hackerrank.

Q1. 
Dice Throws Problem Statement
You are given D dice, each having F faces numbered from 1 to F. The task is to determine the number of possible ways to roll all the dice such that the sum of the face-up num...read more
Ans. 
The task is to determine the number of possible ways to roll all the dice such that the sum of the face-up numbers equals the given 'target' sum.
• Use dynamic programming to solve the problem efficiently.

• Create a 2D array to store the number of ways to achieve each sum with the given number of dice.

• Iterate through the dice and faces to calculate the number of ways to reach each sum.

• Return the result modulo 10^9 + 7.

• Optim...read more
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Q2. 
Stack using Two Queues Problem Statement
Develop a Stack Data Structure to store integer values using two Queues internally.
Your stack implementation should provide these public functions:
Explanation:
...read more
Ans. 
Implement a stack using two queues to store integer values with specified functions.
• Use two queues to simulate stack operations efficiently.

• Maintain the top element in one of the queues for easy access.

• Ensure proper handling of edge cases like empty stack.

• Example: Push elements 5, 10, 15; Pop elements; Check top element.

• Example: Check if stack is empty; Get stack size.
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Q3. 
Maximum Subarray Sum Problem Statement
Given an array 'ARR' of integers with length 'N', the task is to determine the sum of the subarray (including an empty subarray) that yields the maximum sum among al...read more
Ans. 
Find the maximum sum of a subarray in an array of integers.
• Iterate through the array and keep track of the maximum sum subarray ending at each index.

• Use Kadane's algorithm to efficiently find the maximum subarray sum.

• Consider the case where all elements are negative to handle edge cases.

• Example: For input [1, -2, 3, -1, 2], the maximum subarray sum is 4.
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Round duration - 60 minutes
Round difficulty - Medium

My 1st technical round began with a small introduction and a discussion on my projects. After that, my complete interview revolved around Object-oriented principles and 1 basic coding question. The interviewer asked all the pillars of Object-oriented along with use cases and where have I implemented them in my projects.

Round duration - 45 minutes
Round difficulty - Easy

In my 2nd technical round, the interviewer began with the complete discussion of my projects. He asked me everything about them like which technologies I have used, why I have used them, what are the alternatives, what are the real-life applications of these projects. After this, He asked questions on Computer Networks, Operating system, Database Management system and at the end, some of the SQL queries. SQL queries were quite easy, I found one of the queries tricky and harder as compared to other i.e print nth largest salary of the employees. The interviewer also asked me why I was not able to code one of the questions in the coding round. I explained the complete approach of that question and he was quite satisfied. This technical round was of 1 hour long.

Q1. Print the nth largest salary from a given list of salaries.
Ans. 
Find the nth largest salary from a list of salaries.
• Sort the list of salaries in descending order.

• Remove duplicates from the sorted list.

• Return the salary at index n-1 from the sorted list.
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Round duration - 35 minutes
Round difficulty - Easy

There were two panellists in this round and they began with a small introduction. One of them asked me about my capstone project which was based on object recognition task and deep learning. He just told me to explain deep learning in complete detail and how can it be used on my college campus. Then, He asked me what technologies are being used and how I trained my model as the model was quite heavy and computationally intensive. Then, they asked me how I cope up with my team members in the pandemic situation and how I managed to interact with them.

Q1. What are the basic applications of deep learning, and how can I implement it in my college?
Ans. 
Deep learning has applications in image and speech recognition, natural language processing, autonomous vehicles, etc.
• Image recognition - identifying objects in images, used in self-driving cars, medical imaging, etc.

• Speech recognition - converting spoken language into text, used in virtual assistants like Siri, Alexa, etc.

• Natural language processing - understanding and generating human language, used in chatbots, lang...read more
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