18. Interview Questions Asked in MNC Interviews
18.9 Capgemini, Infosys, Cognizant and Wipro
M61. In an online test you are asked to filter data, build a pivot and apply VLOOKUP. How do you approach it?
Reported for: Capgemini [S11]
Convert the data to a Table first, read the task carefully, apply filters with exact criteria, build the pivot from the Table on a new sheet, and write VLOOKUP with FALSE for exact match and absolute references. Check a couple of results manually and watch for #N/A caused by spaces or text numbers.
M62. You are given a raw dataset: clean it and build charts.
Reported for: Capgemini [S11]
Keep a raw copy; remove duplicates, trim and standardise text, fix number and date types, handle blanks; convert to a Table; summarise with a pivot; build a suitable chart (line for trend, bar for comparison) with a clear title and labels; state one insight in words.
M63. How would you use PivotTables and conditional formatting on sales data?
Reported for: Infosys [S12]
Pivot sales by city and month, then apply conditional formatting to the pivot values (Color Scale or Icon Sets; choose "All cells showing Sum of Amount values" so the rule survives refresh). This instantly shows which city-month cells are weak or strong.
M64. Create visual summaries using PivotTables and charts.
Reported for: Cognizant [S13]
Build one pivot per question (sales by city, trend by date, share by platform), insert PivotCharts, hide field buttons, add a slicer connected to all pivots and arrange them on one sheet – a mini dashboard (Module 13).
M65. What is data cleansing?
Reported for: Wipro [S14]
The process of detecting and correcting errors and inconsistencies – duplicates, missing values, wrong types, inconsistent spellings, extra spaces, invalid entries and outliers – so the analysis is accurate and trustworthy. It includes documenting the steps and reconciling counts and totals before and after.
M66. Can VLOOKUP or HLOOKUP be used for data cleansing?
Reported for: Wipro [S14]
They are lookup functions, not cleaning functions, but they support cleansing: a lookup against a mapping table standardises values ("Aurangabad" → "Sambhaji Nagar"), and a lookup against a master list exposes invalid or missing codes through #N/A. Actual text cleaning is done with TRIM, CLEAN, PROPER, SUBSTITUTE or Power Query.
Ravindra Bagale's Tip
In online tests, many students write formulas in a hurry and never check them even once. After every answer, check 2 rows manually; if you see #N/A, first look for spaces and text-vs-number differences. Even when time is short, these 30 seconds save you.
Ravindra Bagale's Tip – मराठी
Online test मध्ये बरेच students घाई-घाईत formula लिहितात आणि एकदाही check करत नाहीत. प्रत्येक उत्तरानंतर 2 rows हाताने तपासा, #N/A दिसला तर आधी spaces आणि text-number फरक बघा. वेळ कमी असला तरी हे 30 सेकंद वाचवतात.
Ravindra Bagale's Tip – हिंदी
Online test में बहुत से students जल्दबाज़ी में formula लिखते हैं और एक बार भी check नहीं करते. हर जवाब के बाद 2 rows हाथ से check करो, #N/A दिखे तो पहले spaces और text-number का फ़र्क देखो. समय कम हो तब भी ये 30 सेकंड बचा लेते हैं.