INDIUM Associate Interview Questions and Answers

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. They check your profile if you are from big company and have more experience they will trap you and hire you and then they will make you their slaves
Ans. Don't take their interview, you will receive their mails in spam. You will loose your peace of mind if you join this horrible company
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Q2. There won't be anything difficult to answer questions. Basic questions like introduce yourself and previous company and experience will be asked. Then if you more experience and qualified they will trap yo...read more
Ans. Don't ever attend the interview nor believe their fake reviews done by their marketing team. Don't even believe LinkedIn updates it's done by their marketing team
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Q1. Introduction and work experience
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Q2. Tool knowledge and its application
Ans. 
I have extensive knowledge of various tools and their practical applications in consulting projects.
• Proficient in using tools like Microsoft Excel, PowerPoint, and Project for data analysis and project management

• Familiar with CRM systems like Salesforce for client relationship management

• Experience with industry-specific tools like Tableau for data visualization

• Ability to adapt and learn new tools quickly to meet projec...read more
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Q1. Reason for job change
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Q1. Basic details and experience
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Q1. Technical round
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Q1. Bagging vs boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q2. Overfitting technique
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Overfitting is when a model learns the training data too well, leading to poor performance on new, unseen data.
• Overfitting occurs when a model is too complex and captures noise in the training data.

• It can be mitigated by using techniques like cross-validation, regularization, and early stopping.

• Examples of overfitting include a decision tree with too many branches or a neural network with too many hidden layers.
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Q1. Discrete vs continuous
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Discrete variables can only take specific values, while continuous variables can take any value within a range.
• Discrete variables are countable and have distinct values, such as number of students in a class.

• Continuous variables can take any value within a range, such as height or weight.

• Discrete variables are often represented by integers, while continuous variables are represented by real numbers.
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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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