Infosystems Web Developer Interview Questions and Answers

Round duration - 60 Minutes
Round difficulty - Easy

Q1. 
0/1 Knapsack Problem Statement
A thief is planning to rob a store and can carry a maximum weight of 'W' in his knapsack. The store contains 'N' items where the ith item has a weight of 'wi' and a value of...read more
Ans. 
Yes, the 0/1 Knapsack problem can be solved using dynamic programming with a space complexity of not more than O(W).
• Use a 1D array to store the maximum value that can be stolen for each weight capacity from 0 to W.

• Iterate through each item and update the array based on whether including the item would increase the total value.

• The final value in the array at index W will be the maximum value that can be stolen.
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Q2. 
Find the Second Largest Element
Given an array or list of integers 'ARR', identify the second largest element in 'ARR'.
If a second largest element does not exist, return -1.
Example:
Input:
ARR = [2,...read more
Ans. 
Find the second largest element in an array of integers.
• Iterate through the array to find the largest and second largest elements.

• Handle cases where all elements are identical.

• Return -1 if a second largest element does not exist.
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Round duration - 60 Minutes
Round difficulty - Easy

System Design Round

Q1. Design a system for Twitter, discussing its architecture, key components, and scalability considerations.
Ans. 
Design a scalable system for Twitter with key components and architecture.
• Use microservices architecture for scalability and fault isolation.

• Key components include user service, tweet service, timeline service, and notification service.

• Use a distributed database like Cassandra for storing tweets and user data.

• Implement a message queue like Kafka for handling real-time updates and notifications.

• Use a caching layer like ...read more
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Round duration - 30 Minutes
Round difficulty - Easy

It is just a formality

Q1. Explain random forest
Ans. 
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random...read more
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Q2. What is boosting
Ans. 
Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
• Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.

• Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

• Boosting is used to improve the accuracy of models and reduce bias and variance.
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Create a business requirement document

Q1. What are the strengths and weakness
Ans. 
Strengths include strong analytical skills and attention to detail. Weaknesses may include difficulty with public speaking and time management.
• Strengths: strong analytical skills

• Strengths: attention to detail

• Weaknesses: difficulty with public speaking

• Weaknesses: time management
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Q1. SOLID principles
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Q2. Microservice Architecture and Kafka
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Q1. Will ask sql queries
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Q2. Oops and design pattern
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Basic react, JS and CSS related questions were there.

Q1. Basic technical question on react, js and css
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Q1. Only salary discussion was done.
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They will give two types of questions on coding you need to write correct logic not depend upon the execution.

Q1. It's technical round they will ask oops concepts , and then collections, spring boot and microservice related questions.
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Q1. Just need to discuss package with HR
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