Walk-in drive written test for analysts, a timed practice paper

Plan for the 45 minutes

The paper

Section A: aptitude

1. A product's price rises 20% and then falls 20%. What is the net change?

2. A team of 6 analysts clears a backlog in 10 days. How many days would 4 analysts take, at the same rate?

3. The average order value of 40 orders is ₹750. One order of ₹2,350 is found to be a duplicate and removed. What is the new average?

4. Sales grew from ₹80 lakh to ₹1 crore. What is the percentage growth?

Section B: data interpretation

Branch sales in ₹ lakh (hypothetical):

5. Which branch had the highest growth from Q1 to Q3, in percentage terms?

6. What share of total Q2 sales came from Noida, to one decimal place?

7. In which quarter was the combined total highest?

Section C: Excel

8. Column A has branch names and column C has sales. Write a formula for total sales of the Noida branch.

9. Write a formula to look up the manager name for the branch in cell F2, from a table with branch in column A and manager in column D of a sheet named Branches, returning "Not found" if missing.

Section D: SQL

Tables: employees(emp_id, name, dept, salary) and sales(emp_id, sale_date, amount).

10. List each department with its number of employees and average salary, highest average first.

11. Find employees who made no sales.

12. Find the second-highest salary.

Answers and workings

1. 1.2 × 0.8 = 0.96, so a 4% decrease. The trap is answering "no change".

2. Work = 6 × 10 = 60 analyst-days; 60 ÷ 4 = 15 days.

3. Total = 40 × 750 = ₹30,000. Remove ₹2,350: ₹27,650 over 39 orders = ₹708.97, about ₹709.

4. ₹1 crore = ₹100 lakh. Growth = (100 − 80) ÷ 80 = 25%. The trap is mixing units.

5. Noida 45/42 → 7.1%; Ghaziabad 36/30 → 20%; Faridabad 30/25 → 20%. Ghaziabad and Faridabad tie at 20%. Papers sometimes include ties to check that you calculate rather than eyeball.

6. Q2 total = 48 + 27 + 30 = 105. Noida share = 48 ÷ 105 = 45.7%.

7. Q1 = 97, Q2 = 105, Q3 = 111. Q3.

8. =SUMIFS(C:C, A:A, "Noida") — or =SUMIF(A:A, "Noida", C:C). Note the argument order differs between the two.

9. =XLOOKUP(F2, Branches!A:A, Branches!D:D, "Not found"), or in older versions =IFERROR(VLOOKUP(F2, Branches!A:D, 4, FALSE), "Not found"). The FALSE for exact match is what examiners look for.

10–12. The SQL key below runs on a small sample so you can check your own answers:

CREATE TABLE employees (emp_id INTEGER, name TEXT, dept TEXT, salary INTEGER);
CREATE TABLE sales (emp_id INTEGER, sale_date TEXT, amount INTEGER);
INSERT INTO employees VALUES
 (1,'Asha','Sales',52000),(2,'Ravi','Sales',48000),(3,'Meena','Ops',61000),
 (4,'Imran','Ops',61000),(5,'Kavya','Finance',70000);
INSERT INTO sales VALUES
 (1,'2026-08-01',15000),(1,'2026-08-09',9000),(2,'2026-08-03',12000),(3,'2026-08-05',4000);

-- Q10
SELECT dept, COUNT(*) AS employees, ROUND(AVG(salary)) AS avg_salary
FROM employees GROUP BY dept ORDER BY avg_salary DESC;

-- Q11
SELECT e.name
FROM employees e LEFT JOIN sales s ON s.emp_id = e.emp_id
WHERE s.emp_id IS NULL;

-- Q12: second-highest distinct salary
SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

Q10 returns Finance 70,000, Ops 61,000, Sales 50,000. Q11 returns Imran and Kavya. Q12 returns 61,000, the second-highest distinct value, even though two employees share it. "Sort descending and take row two" happens to work on this sample, but it fails whenever the top salary is tied, so examiners prefer the MAX ... WHERE salary < form or DENSE_RANK. If asked for the employees earning it, wrap the query and filter salary = that value.

On the day

Carry a printed CV, a pen and photo ID. Arrive early — walk-ins often run in batches, and later batches may wait hours. If negative marking applies, skip questions you would only guess. After the test, candidates who clear it usually go to a technical and then an HR interview the same day, so prepare for the telephonic and HR screening questions too.

For more SQL practice, see SQL join and NULL questions. Aptitude-style data interpretation, Excel and SQL are all practised with timed exercises in the Data Analyst programme.