01
What is normalisation?
Show answer
Organising tables to reduce redundancy and anomalies by splitting data into related tables, commonly up to third normal form.
Bajaj Finserv Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Bajaj Finserv hiring for Data Analytics roles
Bajaj Finserv is a Pune-based financial services group, part of the Bajaj Group, with businesses in lending, insurance and investments. Its subsidiary Bajaj Finance is a large non-banking finance company known for consumer durable loans, personal loans and EMI cards. It hires for credit, risk, analytics, sales and technology roles, including data and digital product teams.
For Data Analytics roles, this Bajaj Finserv practice set focuses most on Tableau (7), SQL (5), Power BI (5), Web & Marketing Analytics (5). It has 1 easy, 29 medium, 10 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Fintech
- Headquarters
- Pune, India
- Main India offices
- Pune
Bajaj Finserv 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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02
What are field parameters in Power BI?
Show answer
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
03
Explain the common marketing attribution models.
Show answer
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.
04
What is the difference between frequentist and Bayesian A/B testing?
Show answer
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.
05
How do you merge two DataFrames on different column names?
Show answer
pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.
06
What is the difference between relationships, joins and data blending in Tableau?
Show answer
Relationships, the default logical layer, connect tables flexibly and Tableau decides join type and level per view, avoiding duplicated measures. Joins physically merge tables into one at a fixed level, which can duplicate rows if grains differ. Blending combines data from separate data sources at the view level using a primary and secondary source, aggregating the secondary first. Prefer relationships, use joins for specific row-level needs, and blending as a last resort.
07
How do you define and choose KPIs?
Show answer
Pick metrics tied to a business goal that are measurable, actionable and owned by someone, and pair a lagging metric with a leading one.
08
How do you optimise a slow SQL query?
Show answer
Read the execution plan, add or fix indexes, select only needed columns, filter early, avoid functions on indexed columns, and replace correlated subqueries with joins.
09
Write pandas code to calculate customer acquisition cost and payback period in months for each channel.
Show answer
# spend: channel, spend
# customers: channel, customer_id, monthly_margin
new_cust = customers.groupby('channel').agg(
new_customers=('customer_id', 'nunique'),
avg_margin=('monthly_margin', 'mean'))
summary = spend.groupby('channel')['spend'].sum().to_frame().join(new_cust)
summary['cac'] = summary['spend'] / summary['new_customers']
summary['payback_months'] = summary['cac'] / summary['avg_margin']
print(summary.round(1).sort_values('payback_months'))
10
How do you handle a dataset with 30% missing values in a key column?
Show answer
Understand why it is missing, consider imputation or a missing flag, test sensitivity, and talk to data owners about fixing collection if it is systematic.
11
Write SQL to find users who logged in on at least 3 consecutive days.
Show answer
WITH d AS (
SELECT DISTINCT user_id, CAST(login_time AS DATE) AS dt
FROM logins
),
g AS (
SELECT user_id, dt,
dt - CAST(ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY dt) AS INT) AS grp
FROM d
)
SELECT DISTINCT user_id
FROM g
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;
-- gaps and islands: consecutive dates share the same grp value
12
Tell me about a time your analysis was challenged in a meeting.
Show answer
Situation: while presenting regional sales, a manager said my numbers were wrong because his team's tracker showed more. Task: handle it without derailing the meeting. Action: I acknowledged the concern, explained my source and definition briefly, and offered to reconcile after the meeting. That afternoon we found his tracker counted orders before cancellations. Result: we agreed on net orders as the standard, and he later asked me to rebuild his tracker.
13
What is Tableau Prep used for?
Show answer
Tableau Prep is a visual tool for cleaning and shaping data before analysis. You build a flow of steps: connect inputs, clean fields, split columns, group similar values, pivot, join, union and aggregate, and see the data change at every step. The output can be an extract or published data source that dashboards use. It suits analysts who need repeatable preparation without writing SQL or Python.
14
How do you handle outliers in Pandas?
Show answer
Detect with IQR or z-score, then cap with clip, remove, transform with log, or keep and flag them depending on whether they are errors or real events.
15
Your pandas script shows SettingWithCopyWarning and a new column does not update. What is happening and how do you fix it?
Show answer
It warns that you may be modifying a temporary copy of a slice, so the change might not reach the original DataFrame, for example df[df.sales > 0]['flag'] = 1. Avoid it by using a single loc call, df.loc[df.sales > 0, 'flag'] = 1, or by calling .copy() when you intentionally create a new subset. Newer pandas versions with copy-on-write make behaviour more predictable, but clear loc assignments remain best practice.
16
How do you handle errors like #N/A and #DIV/0!?
Show answer
Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.
17
Write a DAX measure for a KPI card showing achievement against target and a colour for conditional formatting.
Show answer
Achievement % =
DIVIDE([Total Sales], [Sales Target])
KPI Colour =
VAR a = [Achievement %]
RETURN
SWITCH(
TRUE(),
ISBLANK(a), "#9E9E9E",
a >= 1, "#2E7D32",
a >= 0.9, "#F9A825",
"#C62828"
)
-- apply KPI Colour via Format > Callout value > fx > Field value
18
Write Python code to test whether conversion differs between control and treatment.
Show answer
from statsmodels.stats.proportion import proportions_ztest, proportion_confint
conversions = [1180, 1295] # control, treatment
visitors = [30000, 30100]
z, p = proportions_ztest(conversions, visitors)
print(f'z = {z:.2f}, p = {p:.4f}')
for c, n in zip(conversions, visitors):
low, high = proportion_confint(c, n, method='wilson')
print(f'rate {c/n:.3%} 95% CI {low:.3%} to {high:.3%}')
19
The CMO asks which marketing channel should lose 20% of its budget. How do you approach it?
Show answer
I would compare channels on incremental return, not last-click ROAS. Gather spend, attributed conversions under two models, customer quality such as repeat rate and margin, and any lift test or mix model results. Look at diminishing returns: the last rupee in a saturated channel is worth less than the average. Then recommend cutting where marginal return is lowest, ideally phased with a holdout to confirm the impact.
20
Your Tableau numbers change unexpectedly when a filter is applied to a view that uses a FIXED LOD. Why?
Show answer
Usually it is the order of operations. FIXED LODs are computed before dimension filters, so they ignore a normal region filter, while other measures in the view respect it. That mix makes percentages and totals look inconsistent. Decide what the business wants: if the LOD should respect the filter, add the filter to context; if it should not, keep it and label it clearly, for example share of national sales.
21
Tell me about a time requirements kept changing on a report you were building.
Show answer
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
When do you use a chi-square test?
Show answer
A chi-square test of independence checks whether two categorical variables are related, using a contingency table of counts. Example: is preferred payment method related to city tier? It compares observed counts with the counts expected if there were no relationship. Use it with counts, not percentages, and make sure expected counts are not too small, roughly at least 5 per cell. A significant result shows association, not causation.
23
Explain FIXED, INCLUDE and EXCLUDE LOD expressions in Tableau.
Show answer
Level of Detail expressions compute aggregates at a level different from the view. FIXED computes at the dimensions you specify regardless of the view, for example { FIXED [Customer] : SUM([Sales]) } for lifetime sales per customer. INCLUDE adds a dimension to the view's level, such as average of per-customer sales by region. EXCLUDE removes a dimension, useful for comparing each month to the yearly total. FIXED ignores normal dimension filters unless they are context filters.
24
Write SQL to show each category's percentage share of total revenue.
Show answer
SELECT category,
SUM(amount) AS revenue,
ROUND(100.0 * SUM(amount)
/ SUM(SUM(amount)) OVER (), 2) AS pct_share
FROM sales
GROUP BY category
ORDER BY revenue DESC;
-- SUM(SUM(amount)) OVER () is the grand total across all groups
25
What are relationships and cardinality in Power BI?
Show answer
Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.
26
What is method chaining in pandas and why is it useful?
Show answer
Method chaining writes a sequence of transformations as one expression, each step returning a new DataFrame, for example df.query('sales > 0').assign(margin=lambda x: x.profit / x.sales).groupby('region').agg(total=('sales', 'sum')). It avoids many temporary variables, reads top to bottom like a recipe, and reduces SettingWithCopy issues. Wrap the chain in brackets with one step per line, and use pipe for custom functions.
27
What is the difference between sets and groups in Tableau?
Show answer
A group combines dimension members into higher-level categories, such as merging small states into Others; it is static and each member belongs to one group. A set splits members into In and Out based on a selection or condition, such as top 10 customers by sales, and can be dynamic and recalculate with data. Sets can be combined, used in calculations and changed by set actions, making them more powerful for interactive analysis.
28
Two tables have a many-to-many relationship and your report totals look inflated. What do you do?
Show answer
Inflated totals usually mean rows are being double counted through the many-to-many path. I would first check whether a proper dimension table can sit between them, turning it into two one-to-many relationships, for example a bridge table of unique customer IDs. I would set single filter direction, test totals against the source with a simple SQL query, and use measures with DISTINCTCOUNT where needed. Many-to-many should be a deliberate choice, not a default.
29
What is a factless fact table? Give an example.
Show answer
A factless fact table records that an event or relationship happened but has no numeric measures, only dimension keys. Examples are student attendance (student, class, date) or which products were on promotion in which store each day. You analyse it by counting rows. A promotion coverage table is useful for questions like which promoted products did not sell, by comparing it with the sales fact.
30
How do cookie restrictions, consent rules and server-side tagging affect web analytics data in 2026?
Show answer
Browser tracking prevention, ad blockers and consent banners mean many visits are not fully tracked, so analytics undercounts users and conversions and breaks multi-session journeys. Under India's DPDP Act and similar laws, you need valid consent for non-essential tracking. Teams respond with consent mode modelling, first-party and server-side tagging, logged-in user IDs and reconciling analytics with backend orders, and they treat analytics numbers as estimates rather than exact counts.
31
What is the Central Limit Theorem and why does it matter for analysts?
Show answer
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, usually from about 30 observations, even if the underlying data is skewed. This is why we can use z-tests, t-tests and confidence intervals for averages such as order value or session time. It does not make the raw data normal, and very heavy-tailed data may need larger samples.
32
Why would you publish a Tableau data source separately instead of embedding it in each workbook?
Show answer
A published data source lives on Tableau Server or Cloud and can be reused by many workbooks. Calculations, joins, field names and extract refresh schedules are defined once, so every report uses the same revenue logic and the database is queried once per refresh instead of once per workbook. You can certify it and set permissions. Embedded sources are fine for a quick personal analysis but drift over time.
33
What is Power Query's role in Power BI?
Show answer
It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.
34
A stakeholder wants a 40-column crosstab in Tableau that looks exactly like their Excel sheet. How do you handle it?
Show answer
I would ask what they do with it. If they export and work on it in Excel, a crosstab view with a download option or a scheduled extract is fine, and Tableau should not fight Excel. If they actually scan it for exceptions, I would suggest a summary view with highlights and a drill-down to the detail table. Often both: a short visual summary on top and the detailed table on a second tab.
35
After joining orders to payments, your revenue total doubles. What went wrong?
Show answer
This is a join fan-out: an order has more than one matching payment row, for example split payments or retries, so each order line is repeated and sums are inflated. Check by counting rows per key on both sides. Fix it by aggregating payments to one row per order before joining, joining at the correct grain, or using the right key. Always compare row counts and totals before and after a join.
36
How do SUMIFS and SUMPRODUCT differ?
Show answer
SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
37
Your analysis shows that a flagship initiative championed by leadership is not working. How do you present it?
Show answer
Prepare carefully and stay factual. Share the finding early with the initiative owner privately so they are not surprised in a big meeting. Lead with the question asked, the method and the evidence, acknowledge limitations and what is working, and avoid blaming anyone. Offer options, such as fixing a specific step, narrowing the scope or stopping, with estimated impact. Bad news delivered with solutions is received far better.
38
What is the grain of a fact table and why should you decide it first?
Show answer
The grain is the exact meaning of one row in a fact table, such as one order line, one daily store total or one monthly account balance. You decide it first because it controls which dimensions can join, which measures are valid and how totals behave. Mixing grains in one table, for example order lines and order-level shipping charges, causes double counting. Always state the grain in one sentence before adding columns.
39
Email open rates suddenly jumped from 22% to 45% without any campaign change. Should you celebrate?
Show answer
No. Privacy features in some mail apps pre-load email images, which fires the open tracking pixel even if the person never reads the email, so open rates get inflated. Check opens by email client to confirm. Move reporting towards clicks, click-to-send rate, conversions and unsubscribes, which reflect real behaviour, and avoid using opens alone to pick winning subject lines or to define engaged subscribers.
40
Write SQL to flag rows that fail basic validation rules in a customer table.
Show answer
SELECT customer_id,
CASE WHEN email IS NULL OR email NOT LIKE '%_@_%._%' THEN 1 ELSE 0 END AS bad_email,
CASE WHEN LENGTH(pincode) <> 6 THEN 1 ELSE 0 END AS bad_pincode,
CASE WHEN signup_date > CURRENT_DATE THEN 1 ELSE 0 END AS future_signup,
CASE WHEN age NOT BETWEEN 18 AND 100 THEN 1 ELSE 0 END AS bad_age
FROM customers;
-- sum each flag to report the error rate per rule
Tips to crack the Bajaj Finserv Data Analytics interview
- Learn lending basics such as credit scores, EMI calculation, interest rates and loan approval processes before the interview.
- For analytics and risk roles, revise SQL, Excel and basic statistics, and discuss how data helps assess credit risk.
- Understand the difference between a bank and an NBFC and know Bajaj Finserv's main product lines.
- 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; Bajaj Finserv interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Bajaj Finserv 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 Bajaj Finserv Data Analytics interview?
This practice set leans on Tableau (7), SQL (5), Power BI (5), Web & Marketing Analytics (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Bajaj Finserv interviewers ask you to walk through real work.
Are these the exact questions asked at Bajaj Finserv?
No. These are curated practice questions that match the skills and difficulty usually tested in fintech interviews like Bajaj Finserv's. They are not a leaked or verified list of past questions.
Where does Bajaj Finserv hire Data Analytics professionals in India?
Bajaj Finserv has large teams in Pune. Openings and locations change often, so check the Bajaj Finserv careers page and filter by city and experience.
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