Six Sigma Soft Solutions Capacity Analyst Interview Questions and Answers

1st Round consisted of Technical Discussion and Coding Round. I was asked to write down the logic for Prime no., Fibonacci Series, Matrix related question, etc.

I was asked to share my screen and write down logic for questions related to Strings, Arrays and 2-D matrix's.

Q1. Discussion About the previous projects done. Detailed questions about the technique used and algorithm related questions.
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Q2. Few basic questions about ML. eg: Linear Regression, Logistic Regression, etc.
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The aptitude test contains questions MCQs for quants, basic statistics and basic coding questions. The aptitude also has 2 coding problems to solve which would be of easy-medium level.

The second round was live coding test, where the interviewer looks for your approach towards solving a given problem. The approach matters more than the correct syntax. All coding languages were allowed. SQL queries can also be asked.

Q1. Technical cum HR round contains language specific questions as well questions based on mathematics and statistics. Questions can be little bit tricky.
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Online test with questions from probability and reasoning

A case study for which we need to create a dashboard and a power point presentation.

SQL written test

Q1. Technical round. Interviewed by two member panel.
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Q2. About Tableau Previous projects SQL
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Q1. Common questions like expectations, salary expectations etc.
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Technical Discussion

Technical Discussion with Coding Test

Q1. Previous Job Details
Ans. 
I worked as a project manager at a consulting firm, leading teams to deliver strategic solutions for clients across various industries.
• Managed a team of 10 consultants to deliver a market entry strategy for a tech startup.

• Conducted data analysis to identify key trends, resulting in a 20% increase in client revenue.

• Facilitated workshops with clients to align project goals and expectations, enhancing client satisfaction.

• ...read more
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Q2. Salary discussion
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Q1. What is Supervised machine learning? Which algorithm you think you can explain brilliantly? Why we use random forest then normal decision tree
Ans. 
Supervised machine learning is a type of ML where the algorithm learns from labeled data. Random forest is used over normal decision tree for better accuracy and to avoid overfitting.
• Supervised ML learns from labeled data to make predictions on new data

• Random forest is an ensemble learning method that uses multiple decision trees to improve accuracy

• Random forest is preferred over normal decision tree to avoid overfitti...read more
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Q2. Example of case study
Ans. 
Case study on a successful marketing campaign for a new product launch
• Identified target audience and their needs

• Developed a unique selling proposition

• Created a multi-channel marketing plan

• Implemented the plan and tracked results

• Adjusted strategy based on data analysis

• Achieved high sales and positive customer feedback
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Retail case study , company trends salw.. Total sale. Profit charts and reports