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PoP
## Toronto, Canada
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## Los Angeles, USA
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✅ Excel Interview Questions with Answers📊💼
1️⃣ How do you clean a messy dataset in Excel?
Steps:
- TRIM() → removes extra spaces =TRIM(A1)
- CLEAN() → removes non-printable characters =CLEAN(A1)
- Remove Duplicates → Data → Remove Duplicates
- Text to Columns → split data
- Find & Replace (Ctrl+H) → fix values
- Filter → remove blanks or errors
2️⃣ Absolute vs Relative References
Relative (A1) → changes when copied
Absolute ($A$1) → stays fixed
When to use:
- Relative → normal calculations
- Absolute → fixed values (tax rate, constants)
3️⃣ Create PivotTable for Sales Analysis
Steps:
1. Select data
2. Insert → PivotTable
3. Drag: Region → Rows, Product → Columns, Sales → Values
Used for fast data summarization.
4️⃣ VLOOKUP Formula + #N/A Fix
Formula: =VLOOKUP(A2, Sheet2!A:B, 2, FALSE)
Fix #N/A:
- Check lookup value exists
- Match data types
Use: =IFERROR(VLOOKUP(A2, A:B, 2, FALSE),"Not Found")
5️⃣ INDEX-MATCH vs VLOOKUP
VLOOKUP:=VLOOKUP(A2,A:B,2,FALSE)
INDEX-MATCH:=INDEX(B:B, MATCH(A2,A:A,0))
✅Why INDEX-MATCH?
- Faster for large data
- Works left lookup
- More flexible
6️⃣ COUNTIF vs SUMIF vs COUNTIFS
COUNTIF → count condition =COUNTIF(A:A,"East")
SUMIF → sum condition =SUMIF(A:A,"East",B:B)
COUNTIFS → multiple conditions =COUNTIFS(A:A,"East",B:B,">500")
7️⃣ Goal Seek
Used for what-if analysis.
Steps:
1. Data → What-if Analysis → Goal Seek
2. Set cell → target value
3. Change variable cell
Example: target revenue calculation.
8️⃣ Conditional Formatting Top 10%
Steps: Select data
Home → Conditional Formatting
Top/Bottom Rules → Top 10%
9️⃣ Dynamic Dashboard + Slicers
Create PivotTable
Insert → Slicer
Insert → Timeline (for dates)
Connect slicers to multiple visuals
Used for interactive dashboards.
🔟 SUMPRODUCT (Multi-condition sum)
=SUMPRODUCT((A2:A10="East")(B2:B10>500)C2:C10)
Used for weighted or multiple-condition calculations.
1️⃣1️⃣ What is Power Query?
Excel’s ETL tool.
Steps:
- Get Data → Load data
- Remove columns
- Change types
- Remove duplicates
- Load cleaned data
Used for automation and transformation.
1️⃣2️⃣ Freeze Panes vs Split Panes
Freeze Panes → lock rows/columns while scrolling
Split Panes → divide screen into sections
1️⃣3️⃣ XLOOKUP vs VLOOKUP
XLOOKUP:=XLOOKUP(A2,A:A,B:B)
✅Advantages:
- Left lookup
- No column index
- Default exact match
- Handles errors
1️⃣4️⃣ Circular References Fix
Occurs when formula refers to itself.
Fix:
Formulas → Error Checking → Circular References
Correct formula logic
1️⃣5️⃣ Data Validation + Named Range
Steps:
1. Formulas → Define Name
2. Data → Data Validation → List
3. Select named range
Used for dropdown lists.
Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
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🚀Top 50 Data Analyst Interview Questions📊💼
▎📊EXCEL Questions
1. Can you show me how you'd clean this messy dataset in Excel? What functions like TRIM or Remove Duplicates would you use?
2. What's the difference between absolute ($A$1) and relative (A1) references? When do you use each?
3. Walk me through creating a PivotTable to analyze sales by region and product. What are the exact steps?
4. Write a VLOOKUP formula right now. What if you get #N/A? How do you fix it?
5. Why use INDEX-MATCH over VLOOKUP? Show me both formulas for this lookup.
6. What's COUNTIF vs SUMIF vs COUNTIFS? Write formulas for conditional sales totals.
7. How does Goal Seek work? Demo target revenue scenario on this data.
8. Apply conditional formatting to highlight top 10% sales performers. Which rule?
9. Build me a dynamic dashboard. How do slicers and timelines work together?
10. Explain SUMPRODUCT. Write formula for multi-condition sales sum.
11. What's Power Query? Show basic ETL steps for cleaning data.
12. Freeze panes vs split panes—when do you use each?
13. XLOOKUP vs VLOOKUP advantages? Write both for this example.
14. How do you find and fix circular references in formulas?
15. Create data validation dropdown + named ranges. Demo it.
▎🗄️SQL Questions
16. Write query for 2nd highest salary from Employee table. Use subquery OR window function.
17. INNER JOIN vs LEFT JOIN vs FULL JOIN? Write examples for employees + departments.
18. Find and remove duplicate records. Use CTE + ROW_NUMBER() or GROUP BY.
19. WHERE vs HAVING with GROUP BY? Show department-wise avg salary > 50k.
20. RANK() vs DENSE_RANK() vs ROW_NUMBER()? Partition by dept, order by salary.
21. Top 5 products by total sales. Write complete query with GROUP BY + LIMIT.
22. Self-join for employee-manager hierarchy. Show employee name + manager name.
23. Handle NULL salaries. Use COALESCE, IS NULL, IFNULL examples.
24. Pivot sales data by month using CASE statements. Write query.
25. Subquery vs JOIN—which is faster for this scenario? Why?
26. Recursive CTE for company hierarchy (CEO → managers → employees).
27. Clustered vs non-clustered indexes? When does each improve performance?
▎🎨Tableau Questions
28. {FIXED [Region]: SUM([Sales])}—what's this LOD doing? Write region total ignoring filters.
29. Create dual-axis chart comparing sales vs profit trends. Exact steps?
30. Data blending vs joining? When do you use each approach?
31. Parameters vs filters? Write calculated field using parameter.
32. Build dashboard with filter action + highlight action. Demo flow.
33. % of total calculated field? Write formula for region sales %.
34. FIXED vs INCLUDE vs EXCLUDE LOD? Give 3 examples.
35. Tableau Extracts vs Live connection? Performance + refresh differences?
▎⚡Power BI Questions
36. CALCULATE(SUM(Sales), SAMEPERIODLASTYEAR())—explain this DAX. YoY growth?
37. Measures vs Calculated Columns? When do you use each? Write both.
38. Star schema vs Snowflake? Draw relationships for sales → products → customers.
39. Power Query: Write M code for custom column parsing dates.
40. Implement Row-Level Security (RLS). Show DAX for region manager filter.
41. DirectQuery vs Import mode? Pros/cons + when to choose each?
42. TOTALYTD(SUM(Sales))—explain time intelligence DAX.
43. Dashboard loads slow. Optimization steps? Aggregations + query folding?
▎🐍Python/Pandas Questions
44. Group sales by region and sum: write pandas code. .reset_index()
45. pd.merge(df1, df2, on='ID', how='inner')—explain all merge types.
46. Three ways to handle NaN values: fillna(), dropna(), interpolate().
47. loc[] vs iloc[]? Filter sales > 1000 by region vs first 5 rows.
48. pivot_table() vs groupby()? Reshape sales by month/product.
49. Read 1GB CSV without crashing: chunksize=10000 example.
50. df['New'] = df['Sales'].apply(lambda x: x*1.1)—alternatives to apply?
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Функция РАЗВЕРНУТЬ / EXPAND
Что она делает? Увеличивает размеры массива. Все "дополнительные" значения (то есть дополнительные строки и/или столбцы, то, чего нет в исходном массиве, который задается в первом аргументе функции) будут ошибками #Н/Д (#N/A).
Но их можно заменить на какое-то значение — указав его в четвертом аргументе.
Вот пример, как мы используем эту функцию, чтобы при сборе топ-N сделок из разных таблиц формировать дополнительный столбец, в котором будет имя каждой таблицы.
В предыдущем варианте — по ссылке — мы использовали для этого другую функцию MAKEARRAY.
Оберни колонки: новая (относительно) функция WRAPCOLS
Итак, нам с вами нужно превратить одномерный массив — например, столбец, в котором данные цикличные (время начала мероприятия + N строк с выступающими в нашем примере) — в двумерный, разместив каждый повторяющийся "блок" в отдельный столбец.
Засунем диапазон в WRAPCOLS, вторым аргументом укажем, сколько ячеек отправлять в каждый столбец. Необязательный третий аргумент — как возвращать пустые ячейки из исходника, если они там будут. Иначе будет выводиться ошибка #N/A (#Н/Д).
=WRAPCOLS(A1:A;N; [чем заменить пустые])
Можно и открытый диапазон использовать, но тогда справа от функции ничего нельзя будет вводить вручную, так как она будет требовать много-много столбцов. Можно фильтровать с помощью FILTER, оставляя только заполненные ячейки.
=WRAPCOLS(FILTER(A1:A;A1:A<>"");N)
P.S. Раз есть функция WRAPCOLS — значит — это кому-нибудь нужно? есть и WRAPROWS.
P.P.S. В Excel (365) при русскоязычном интерфейсе — СВЕРНСТОЛБЦ и СВЕРНСТРОК.
N-Y53745 FTDNA / N-BY69898 Yfull
Башкир племени Минг из с.Киргиз-Мияки, Миякинский рн, Башкортостан.
Ближайшее ДДНК: мадьяр эпохи «Обретения Родины» Őrhalom, Sárrétudvari, Hungary; Кушнаренковская культура могильник «Уелги» Челябинская обл.
Среди совпавших ближе всех башкиры из племени Минг, разделенные в IX-X вв. и мишар из Нижнего Новгорода, с которым Минги были разделены в VI-VII вв.н.э.
Также близки башкиры из племен Елан и Юрматы, татары из д. Именьково Лаишевского рна РТ и современные венгры, разделение с которыми произошло во II в.н.э.
Благодаря полногеномному тесту была установлена Mt-ДНК U4D2 , которая была также выявлена среди ДДНК мадьяр эпохи «Обретения Родины».
#Мең#N (N-BY69898)