MathCo Senior Cloud Engineer Interview Questions and Answers

Q1. Basic QA related Questions
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Q1. Questions related to SAP BW and HANA
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Q1. Questions related to SAP BW and HANA
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Q1. Reason for Job Change
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90 min coding test with 4-5 problems to solve

Q1. Technical rounds mostly goes around the project work ans some were based on the JD.
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Q1. Python, Django questions Easy to Medium level DSA Discussion on Project and previous work experience Some SQL queries
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Q1. Some Python questions and discussion on work experience Some design question like how you will design backend for some project from scratch Some managerial questions Questions on git
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Q1. Some basic HR questions
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Q1. Basic mern stack question along with some coding question.
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Q1. Same as round one. This one is in bit details.
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Q1. Basic hr round. Lot of different aspect they gave.
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In Mu Sigma APT round is divided into 3 sections Quant, Logical and verbal

Q1. Explain oops concept
Ans. 
OOP is a programming paradigm based on the concept of objects, which can contain data in the form of fields and code in the form of procedures.
• OOP focuses on creating objects that interact with each other to solve problems.

• Key concepts include classes, objects, inheritance, polymorphism, and encapsulation.

• Classes are blueprints for creating objects, while objects are instances of classes.

• Inheritance allows a class to i...read more
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Coding was good and it was based on bubble sort

Round duration - 120 minutes
Round difficulty - Easy

After round 1 there was a break of 15 mins and the coding round started.
We had a choice to attempt the exam in R language & SQL or python & sql
I chose python and sql
It had 8 questions 4 python and 4 sql

Q1. 
Longest Common Subsequence Problem Statement
Given two strings, S and T with respective lengths M and N, your task is to determine the length of their longest common subsequence.
A subsequence is a seque...read more
Ans. 
The task is to find the length of the longest common subsequence between two given strings.
• Use dynamic programming to solve this problem efficiently.

• Create a 2D array to store the lengths of longest common subsequences of substrings.

• Iterate through the strings to fill the array and find the length of the longest common subsequence.

• Example: For strings 'abcde' and 'ace', the longest common subsequence is 'ace' with leng
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Round duration - 15 minutes
Round difficulty - Medium

The interview was mostly analytical based and how you approach the round a few technical questions in dbms and oops were also asked.

Round duration - 20 Minutes
Round difficulty - Medium

Similar to first round
For Shortlists after elimination from first round.

Q1. 
Car Pooling Capacity Problem
You are a cab driver with a car that initially has 'C' empty seats. The car moves in a straight line towards the forward direction only. Your job is to determine if it is poss...read more
Ans. 
Determine if it is possible to accommodate all passenger trips within a car's capacity without exceeding it at any point.
• Iterate through each trip and keep track of the total number of passengers in the car at each point.

• Check if the total number of passengers exceeds the car capacity at any point.

• Return 'True' if all trips can be accommodated within the car capacity, otherwise return 'False'.
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Round duration - 20 minutes
Round difficulty - Easy

After two technical round interviews, the last round was a hr interview. The whole process can take up one whole day or more and your interview can be at any time
My process started at 5 pm and ended at 10:30 pm
All the interviewers were very friendly no pressure.

Q1. 
Nth Fibonacci Number Problem Statement
Calculate the Nth term in the Fibonacci sequence, where the sequence is defined as follows: F(n) = F(n-1) + F(n-2), with initial conditions F(1) = F(2) = 1.
Input:
...read more
Ans. 
Calculate the Nth Fibonacci number efficiently using dynamic programming.
• Use dynamic programming to store previously calculated Fibonacci numbers to avoid redundant calculations.

• Start with base cases F(1) and F(2) as 1, then iterate to calculate subsequent Fibonacci numbers.

• Time complexity can be optimized to O(N) using dynamic programming.

• Example: For N = 5, the 5th Fibonacci number is 5.
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