Kotak Mahindra Bank Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Kotak Mahindra Bank hiring for Data Analytics roles
Kotak Mahindra Bank is a major Indian private sector bank offering retail banking, corporate banking, wealth management, credit cards and digital banking through products like Kotak 811. The wider Kotak group also includes securities, asset management and insurance businesses. It hires for relationship management, operations, risk, analytics and technology roles across its offices and branches.
For Data Analytics roles, this Kotak Mahindra Bank practice set focuses most on Excel (7), SQL (7), Python for Analysis (6), Tableau (4). It has 7 easy, 27 medium, 6 hard questions, so you can start easy and work up to the harder ones.
Company type
Banking & finance
Headquarters
Mumbai, India
Main India offices
Mumbai, Bengaluru
Kotak Mahindra Bank Data Analytics interview process
Online assessment or resume screening
Functional or technical interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
What is the difference between extract filters and data source filters in Tableau?
Extract filters decide which rows go into the extract file at all, which reduces its size and speeds every view; filtered rows are simply not available. Data source filters apply to all worksheets using that data source, for live or extract, and are useful for security or scope, such as only the India business. Both act before context filters. Normal worksheet filters only affect one sheet.
A north star metric is the single measure that best captures the value a product delivers to customers and predicts long-term growth, such as weekly active learners for an education app or orders delivered for a delivery service. It aligns teams around one outcome. It should be supported by input metrics that teams can influence, such as activation rate and repeat usage, and balanced with guardrails like cost or quality.
Attribution decides how credit for a conversion is shared across touchpoints. Last click gives all credit to the final touch, first click to the first one. Linear splits credit equally, time decay gives more to recent touches, and position-based gives most to first and last. Data-driven attribution uses an algorithm on your own paths to estimate each channel's contribution. Each model answers a different question, so state which one a report uses.
A junk dimension combines several low-cardinality flags and indicators, such as payment type, is_gift, is_express and order channel, into one small dimension table. Each row is a unique combination of those values, and the fact table stores a single key to it. This keeps the fact table narrow and avoids many tiny dimensions or loose flag columns. It works best when the combinations are limited, typically a few hundred rows.
05
Write pandas code to calculate monthly revenue from an orders DataFrame.
Dynamic array formulas such as FILTER, UNIQUE and SORT return several values that spill into neighbouring cells. #SPILL! appears when that spill range is blocked by existing data, merged cells, or when the formula sits inside an Excel Table, which does not allow spilling. Fix it by clearing the blocking cells, unmerging, or moving the formula outside the Table. Select the error cell to see the dashed border of the range it needs.
The Data Model is an in-memory columnar store inside Excel that holds several related tables, and Power Pivot is the interface to manage it. Instead of flattening data with VLOOKUPs, you load fact and dimension tables, create relationships and write DAX measures. It handles millions of rows that would not fit on a sheet, and PivotTables built on it can slice across tables. It is the same engine idea that Power BI uses.
First confirm it is real: check tracking events, data delays and whether the denominator changed. Then segment by app version, platform, device, payment method and traffic source. If the drop is concentrated in the new version or one payment option, it points to a bug or UX change. Look at funnel steps to find where users drop. Compare with seasonality and campaigns, then share findings with product and engineering quickly.
16
Write a CREATE TABLE statement for a sales fact table linked to date, product and store dimensions.
CREATE TABLE fact_sales (
date_key INT NOT NULL REFERENCES dim_date(date_key),
product_key INT NOT NULL REFERENCES dim_product(product_key),
store_key INT NOT NULL REFERENCES dim_store(store_key),
invoice_no VARCHAR(20) NOT NULL, -- degenerate dimension
quantity INT NOT NULL,
net_amount DECIMAL(12,2) NOT NULL,
discount_amount DECIMAL(12,2) DEFAULT 0
);
A conformed dimension is one dimension table with the same keys, attributes and meanings shared by several fact tables. For example, one date and one product dimension used by sales, returns and inventory facts. Because they conform, you can compare sales and returns by the same product category in one report without mismatched definitions. Conformed dimensions are the main way an enterprise warehouse stays consistent across departments.
// Calculated field: Last 30 Days Flag
DATEDIFF('day', [Order Date], TODAY()) <= 29
AND [Order Date] <= TODAY()
// Put this field on Filters and keep True.
// For a sales measure instead of a filter:
// IF DATEDIFF('day', [Order Date], TODAY()) <= 29 THEN [Sales] END
21
Tell me about a time requirements kept changing on a report you were building.
Situation: a sales head kept adding new metrics while I was building a Power BI report. Task: deliver on time without endless rework. Action: I wrote a one-page scope with the agreed KPIs, shared a rough mock-up first, and logged new asks in a backlog with effort estimates. We agreed phase one and phase two. Result: phase one went live on schedule, and later requests were planned instead of disrupting the work.
22
Write an Excel formula to look up a price using two criteria, product and city.
=XLOOKUP(1, (A2:A500=G2) * (B2:B500=H2), C2:C500, "Not found")
-- A = product, B = city, C = price
-- G2 and H2 hold the product and city to find
-- each condition returns TRUE/FALSE; multiplying gives 1 only where both match
MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.
25
A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?
Totals in DAX are not the sum of visible rows; the measure is recalculated in the total's filter context. A measure like a distinct count, a ratio or an IF condition can give a different result at total level. If the business wants the sum of row values, iterate explicitly, for example SUMX ( VALUES ( Product[Name] ), [My Measure] ). Confirm with stakeholders which total is correct before changing it.
26
What does the IF function do and how do you nest it?
Separate input cells from calculation cells and colour inputs clearly. Unlock only the input cells through Format Cells, Protection, then protect the sheet so formulas cannot be edited, and optionally hide formulas. Add data validation on inputs to block wrong values such as negative quantities. Protect workbook structure so sheets cannot be deleted, keep a version history, and add a short instructions sheet so users know where to type.
28
How do you present insights to non-technical stakeholders?
Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.
29
Which KPIs would you include in an HR attrition dashboard?
Core KPIs: headcount, monthly and annualised attrition rate, voluntary versus involuntary split, and early attrition within the first 90 days or first year. Breakdowns by department, location, manager, tenure band, performance rating and salary band show where it concentrates. Add hiring metrics such as time to fill and offer acceptance rate, and exit reasons from exit interviews. Protect privacy by hiding very small groups.
30
Write SQL to find the top 3 products by revenue within each category.
WITH ranked AS (
SELECT category,
product,
SUM(amount) AS revenue,
DENSE_RANK() OVER (
PARTITION BY category
ORDER BY SUM(amount) DESC
) AS rnk
FROM sales
GROUP BY category, product
)
SELECT category, product, revenue
FROM ranked
WHERE rnk <= 3
ORDER BY category, rnk;
31
What is the difference between frequentist and Bayesian A/B testing?
Frequentist testing asks how surprising the data would be if there were no difference, and gives p-values and confidence intervals with a fixed sample size. Bayesian testing combines a prior with the data to give a probability that treatment beats control and the expected loss of choosing it. Bayesian results are easier for business users to read, but still depend on priors and still need sensible stopping rules.
32
How do you implement row-level security in Tableau?
The scalable approach is an entitlement table mapping usernames to allowed values, such as region managers to their regions. Relate or join it to the data, then add a calculated filter like USERNAME() = [Username] and apply it as a data source filter on the published source so it cannot be removed. For small cases, user filters or group-based functions like ISMEMBEROF work. Test by viewing as different users before release.
33
What is a transaction and what do the ACID properties mean?
A transaction is a group of SQL statements that succeed or fail together, such as debiting one account and crediting another. ACID: Atomicity means all or nothing; Consistency means rules and constraints stay valid; Isolation means concurrent transactions do not see each other's partial work; Durability means committed data survives crashes. Analysts mostly read data, but knowing ACID explains why reports should read committed data.
concat stacks DataFrames vertically (more rows) or side by side along an axis, for example appending monthly files with the same columns. merge combines DataFrames on key columns, like a SQL join, with how='inner', 'left', 'right' or 'outer'. join is a shortcut for merging on the index. Use concat to append and merge to look up related data. Always check row counts after a merge.
A Power Query table is built during data refresh using M, before data enters the model, and is compressed like any imported table. A DAX calculated table is computed after load from other model tables, using functions such as CALENDAR, SUMMARIZE or DISTINCT. Prefer Power Query for shaping and cleaning source data. Use calculated tables for model-dependent needs, such as a date table from existing data or a small what-if table.
38
Write pandas code to compute month-on-month percentage change and cumulative sales for each store.
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
39
Write a Tableau table calculation for year-over-year sales growth.
// with YEAR(Order Date) in the view, Compute Using: Table (across)
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1))
/ ABS(LOOKUP(ZN(SUM([Sales])), -1))
// the built-in Quick Table Calculation 'Year over Year Growth' generates similar logic
// format the field as a percentage
40
Find customers who ordered in January but not in February.
SELECT DISTINCT customer_id FROM orders WHERE month = 'Jan'
EXCEPT
SELECT customer_id FROM orders WHERE month = 'Feb';
Tips to crack the Kotak Mahindra Bank Data Analytics interview
Revise banking basics such as types of accounts, loans, interest rates, KYC and the role of the RBI.
For technology and analytics roles, prepare SQL, Excel and one programming language, with examples from your projects.
Read about Kotak's digital banking efforts like Kotak 811 and recent banking news to discuss them in interviews.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Kotak Mahindra Bank interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Kotak Mahindra Bank Data Analytics interview?
Candidates usually go through 4 stages: Online assessment or resume screening; Functional or technical interview; Managerial round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Kotak Mahindra Bank Data Analytics interview?
This practice set leans on Excel (7), SQL (7), Python for Analysis (6), Tableau (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Kotak Mahindra Bank interviewers ask you to walk through real work.
Are these the exact questions asked at Kotak Mahindra Bank?
No. These are curated practice questions that match the skills and difficulty usually tested in banking & finance interviews like Kotak Mahindra Bank's. They are not a leaked or verified list of past questions.
Where does Kotak Mahindra Bank hire Data Analytics professionals in India?
Kotak Mahindra Bank has large teams in Mumbai, Bengaluru. Openings and locations change often, so check the Kotak Mahindra Bank careers page and filter by city and experience.