Invergence Analytics Interview Questions and Answers

Technology making ours less human ( 15 min) online

Q1. Tell me about yourself
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Q2. Tell me about you inters hip
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Q3. Fixed assets with example
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Fixed assets are long-term tangible assets that are used in the production of goods or services and are not intended for sale.
• Fixed assets are physical assets such as buildings, machinery, equipment, vehicles, and land.

• They are not intended for sale and are used for the production of goods or services over a long period of time.

• Examples of fixed assets include manufacturing plants, office buildings, delivery trucks, an
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Q4. Capital assets with example
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Capital assets are long-term assets that are used in the production of goods or services and are not easily converted into cash.
• Capital assets are typically tangible assets such as buildings, machinery, equipment, and vehicles.

• They are used by a company to generate revenue over an extended period of time.

• Examples of capital assets include manufacturing plants, delivery trucks, office furniture, and computer systems.
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Q5. Why we use current ration
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Current ratio is used to assess a company's ability to pay its short-term obligations with its short-term assets.
• Current ratio is a liquidity ratio that measures a company's ability to cover its short-term liabilities with its short-term assets.

• It is calculated by dividing current assets by current liabilities.

• A current ratio of 1 or higher is generally considered healthy, as it indicates that a company has enough curr...read more
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Q1. How do you resolve critical issues?
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Q2. How do to handle stakeholder's interests?
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Handle stakeholder's interests by understanding their needs, communicating effectively, and delivering results.
• Understand the stakeholders' needs and priorities

• Communicate regularly and effectively with stakeholders

• Involve stakeholders in decision-making processes

• Deliver results that align with stakeholders' interests

• Manage expectations and address concerns promptly
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SQL Rank functions.
Python easy to medium DSA programs.
Pyspark Concepts and questions.

Q1. Explain one of your projects in End to End
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Q1. What are some of the analytical functions available in SQL?
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Analytical functions in SQL are used to perform calculations on sets of rows.
• Aggregate functions like SUM, AVG, COUNT, MIN, MAX

• Window functions like ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD

• Ranking functions like NTILE, PERCENT_RANK, CUME_DIST

• Statistical functions like STDDEV, VARIANCE

• String functions like CONCAT, SUBSTRING, TRIM
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Q1. Median of 2 sorted array
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Q2. Linux commands for chmod
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Q3. Features of python
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Q4. Deepcopy and shallowcopy
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Q1. What are Covenants in Commercial Lending
Ans. 
Covenants in commercial lending are conditions set by lenders that borrowers must meet to maintain the loan agreement.
• Covenants are financial ratios or performance metrics that borrowers must adhere to

• They are designed to protect the lender by ensuring the borrower remains financially stable

• Examples include debt-to-equity ratio, interest coverage ratio, and minimum liquidity requirements
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Q2. What is the difference between Bank Guarantee and Letter of Credit
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Bank Guarantee is a guarantee from a bank that a borrower will fulfill their financial obligations, while Letter of Credit is a payment method where the bank guarantees payment to the seller.
• Bank Guarantee is a guarantee provided by a bank to a beneficiary in case the applicant fails to fulfill their obligations.

• Letter of Credit is a payment method where the bank guarantees payment to the seller upon presentation of sp...read more
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Q3. What is Collateral in Commercial Lending
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Collateral in commercial lending refers to assets that a borrower pledges to a lender as security for a loan.
• Collateral can include real estate, equipment, inventory, accounts receivable, or other valuable assets.

• The lender can seize and sell the collateral if the borrower defaults on the loan.

• Collateral helps reduce the lender's risk and allows borrowers to access financing at lower interest rates.

• The value of the col...read more
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Q1. Self Introduction
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Q2. Situation based questions like Conflict of interest amongst team members, How to handle in case of high volumes, Any new updates on Secondary Loan Trading
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Q3. What is a Credit Agreement
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A Credit Agreement is a contract between a borrower and a lender outlining the terms and conditions for a loan.
• It specifies the amount of the loan, interest rate, repayment schedule, and any collateral required.

• The agreement also includes provisions for default, late payments, and other consequences.

• Credit agreements can be for various types of loans, such as mortgages, car loans, or personal loans.
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Q1. Java 8 features
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Q2. @transactional
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Q1. Sql questions based on queries and scenarios
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Q2. Microservices architecture
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Q3. Design patterns
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Q1. Normal discussion about experience and salary expectations
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Q2. Scheduled for 2nd round
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Q1. 2 interviewers, 1 of them who’s leaving the organisation in few days. Asked me about how would you strategise the campaigns and tell me about your successful campaign?
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Q1. This round was final with a Senior Manager as told by the HR
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Q2. The question returned to strategies again for the brand I have worked, after answering all the questions. The conversation derailed by the interviewers limiting technical knowledge. Wasted my valuable 50 m...read more
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Q1. This round supposed to be feedback round if hired then salary discussion, however, they conducted 1 more technical round as they had trust issues due to their lacking knowledge.
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Q2. Two Musketeers, took the round. One leaving the company thinking CEO of the company with lots of attitude conducted the interview with a live technical round over screen sharing. At last the CEO didn’t lik...read more
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30 easy aptitude questions.

Q1. Video analysis. Summarize the video in bullet points.
Ans. 
Video analysis involves summarizing the content of a video in bullet points.
• Identify key themes or topics discussed in the video

• Highlight important events or moments

• Note any key statistics or data presented

• Summarize the main conclusions or takeaways

• Include any notable visuals or graphics shown
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Q2. Summarize all the examples you saw in the video.
Ans. 
The video showcased various examples of data analysis and visualization techniques.
• Exploratory data analysis using histograms and scatter plots

• Predictive modeling using regression and classification algorithms

• Time series analysis for forecasting future trends

• Text mining for sentiment analysis

• Clustering techniques for customer segmentation
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Q3. Tell me about yourself
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I am a recent graduate with a degree in Computer Science and a passion for data analysis and machine learning.
• Recent graduate with a degree in Computer Science

• Passionate about data analysis and machine learning

• Experience with programming languages like Python and R

• Completed projects involving data visualization and predictive modeling
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Q4. Discussion on projects.
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Q5. What do you know about mu Sigma?
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Mu Sigma is a data analytics and decision sciences company that helps organizations make data-driven decisions.
• Mu Sigma was founded in 2004 and is headquartered in Chicago, Illinois.

• They provide services in areas such as marketing analytics, supply chain analytics, and risk analytics.

• Mu Sigma has worked with Fortune 500 companies across various industries to help them leverage data for better decision-making.

• The compan...read more
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Q6. Why do you want to join Mu Sigma?
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I want to join Mu Sigma because of their reputation for providing challenging and impactful data science projects.
• Mu Sigma is known for working on cutting-edge data science projects with top clients

• I am excited about the opportunity to work with a diverse team of data scientists and learn from their expertise

• I believe Mu Sigma's focus on innovation and problem-solving aligns with my own career goals
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Q7. SQL and technical questions based on your projects or tech stack.
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Q8. What challenges you faced during your projects?
Ans. 
I faced challenges in data cleaning, feature engineering, and model optimization during my projects.
• Data cleaning: Dealing with missing values, outliers, and inconsistencies in the data.

• Feature engineering: Creating new features from existing data to improve model performance.

• Model optimization: Tuning hyperparameters and selecting the best algorithm for the problem.

• Example: In a project predicting customer churn, I ha...read more
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