State Bank of India Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About State Bank of India hiring for Data Analytics roles
State Bank of India is India's largest public sector bank, with a huge network of branches and digital services such as the YONO app. It offers retail and corporate banking, loans, deposits and government banking services. It typically recruits through public examinations for roles like probationary officer and clerk, and through specialist officer notifications for IT, risk and data roles.
For Data Analytics roles, this State Bank of India practice set focuses most on Power BI (8), A/B Testing & Experiments (5), Data Cleaning (4), Tableau (4). It has 2 easy, 30 medium, 8 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, Navi Mumbai
State Bank of India Data Analytics interview process
Preliminary online exam (for PO or clerk roles)
Main online exam
Psychometric test, group exercise or interview
Document verification and final selection
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
You must present to the sales team that they missed their quarterly target by 18%. How do you handle it?
Lead with the fact clearly, then show where the gap came from, such as which regions, products or deal stages, so it becomes actionable rather than a blame session. Separate controllable causes like lower conversion from uncontrollable ones like a delayed product launch. Highlight what did work, and end with two or three specific actions and the metrics to track them. Share the numbers with sales leaders privately first, to avoid surprises.
02
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
Orphan records are child rows whose foreign key has no matching parent, such as orders with a customer_id that does not exist in the customer table. They break joins silently: an inner join drops them and totals fall short. Find them with a LEFT JOIN from the child to the parent and filter where the parent key IS NULL. Then fix the source, add an Unknown member, or report them separately.
A dataflow is Power Query running in the Power BI Service. It extracts and cleans data once and stores the result so many datasets can reuse it. For example, a single cleaned Customer table can feed sales, finance and marketing reports, ensuring consistent logic and reducing load on the source. Dataflows suit shared, reusable preparation; logic that only one report needs can stay in that report's Power Query.
04
How do you measure impact when a proper A/B test is not possible?
Use quasi-experimental methods. Difference-in-differences compares the change in a treated group, such as cities that got a new policy, with the change in similar untreated cities over the same period. Synthetic control builds a weighted mix of untreated regions to mimic the treated one. Regression discontinuity uses a cut-off, like a credit score threshold. Always check pre-period trends and state the assumptions clearly.
05
Tell me about a time an experiment result went against what the team expected.
Situation: the team was sure a shorter signup form would raise registrations. Task: I owned the test analysis. Action: registrations rose 6%, but I checked the guardrail and found seven-day activation fell, because we had removed a question that personalised onboarding. I shared both results with the segment breakdown. Result: we kept the short form but moved the question to the first screen after signup, which recovered activation.
06
What is referential integrity and how do orphan records appear in analytics data?
Referential integrity means every foreign key value points to an existing row in the parent table, for example every order's customer_id exists in customers. Orphan records break this rule. They appear when source systems load tables at different times, records are deleted without cascading, or IDs are formatted differently across systems. Detect them with a LEFT JOIN where the parent key is NULL, and decide whether to fix, map or flag them.
07
Write pandas code to find the top 2 products by revenue in each region.
-- create an inactive relationship Sales[ShipDate] -> 'Date'[Date] in the model
Sales by Ship Date =
CALCULATE(
[Total Sales],
USERELATIONSHIP(Sales[ShipDate], 'Date'[Date])
)
-- the measure uses the inactive relationship only inside this CALCULATE
-- the alternative is a second role-playing date table
09
What is the difference between relationships, joins and data blending in Tableau?
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.
10
You loaded a month of sales into the warehouse. How do you check the load is complete and correct?
Reconcile against the source: compare row counts, total amount, and counts by day and by store between source and target. Check for duplicate keys, nulls in mandatory columns, and dates outside the expected month. Compare with the previous month to spot big unexplained jumps. Spot-check a few individual transactions end to end. Automate these checks so each future load produces a small pass or fail report.
A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).
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.
13
Write SQL to find the first-touch and last-touch channel for every user who converted.
WITH touches AS (
SELECT t.user_id, t.channel, t.touch_ts,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts) AS first_rank,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts DESC) AS last_rank
FROM marketing_touches t
JOIN conversions c
ON c.user_id = t.user_id
AND t.touch_ts <= c.converted_ts
)
SELECT user_id,
MAX(CASE WHEN first_rank = 1 THEN channel END) AS first_touch,
MAX(CASE WHEN last_rank = 1 THEN channel END) AS last_touch
FROM touches
GROUP BY user_id;
14
What is the order of operations in Tableau and why does it matter?
Tableau applies filters in a fixed order: extract filters, data source filters, context filters, then FIXED LOD calculations, dimension filters, INCLUDE and EXCLUDE LODs, measure filters, and finally table calculations and table calc filters. It matters because a calculation only sees data filtered before it. For example, a FIXED LOD ignores a normal region filter unless that filter is added to context. Many wrong totals come from not knowing this order.
15
Write Python code to calculate the sample size per group for a conversion test with a 4% baseline and a 10% relative lift.
Both compare means. A z-test assumes the population standard deviation is known or the sample is large, so the normal distribution applies. A t-test estimates standard deviation from the sample and uses the t-distribution, which has heavier tails for small samples. In practice analysts mostly use t-tests: one-sample, independent two-sample (Welch's version if variances differ) or paired, for before-after measurements on the same users.
17
Checkout conversion fell from 3.2% to 2.6% the week after an app release. How do you investigate?
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.
18
A stakeholder wants a 40-column crosstab in Tableau that looks exactly like their Excel sheet. How do you handle it?
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.
Sales LY =
CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
Sales YoY % =
VAR cur = [Total Sales]
VAR prev = [Sales LY]
RETURN
DIVIDE(cur - prev, prev)
-- needs a marked date table; format the measure as a percentage
=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
23
What is statistical significance vs practical significance?
A good answer links genuine interest, evidence and the role. For example: I enjoy turning messy questions into clear answers. In my final-year project I analysed two years of canteen sales in Excel and Python and showed that changing the menu on exam weeks could cut waste. I liked that the analysis led to an actual change. I want a role where my SQL, Power BI and communication skills help teams make better decisions.
Row context evaluates one row at a time (calculated columns); filter context is the set of filters from slicers, visuals and relationships that a measure sees.
27
What are statistical power and minimum detectable effect (MDE)?
Power is the probability that the test detects a real effect of a given size, commonly set at 80%. The minimum detectable effect is the smallest lift you care about and can reliably detect with your sample size. Smaller MDEs, lower baseline rates and higher power all need more users. If you can only get 10,000 users a week, you may only be able to detect large lifts, which should shape what you test.
28
Analytics shows 1,820 purchases last month but the order database shows 2,150. Which is right, and how do you explain the gap?
The order database is the source of truth for counts and revenue. Analytics usually undercounts because of ad blockers, declined cookie consent, users closing the page before the confirmation tag fires, and payment redirects that break sessions. It can also overcount from duplicate tags on page refresh. Quantify the gap per month; if it is stable around 15%, use analytics for channel mix and trends and the database for totals.
29
You share a forecasting workbook and users keep overwriting formulas. How do you protect 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.
30
What are additive, semi-additive and non-additive facts?
Additive facts, like sales amount or quantity, can be summed across every dimension. Semi-additive facts, like account balance or stock on hand, can be summed across accounts or stores but not across time; for time you take the closing or average value. Non-additive facts, like percentages, ratios or unit prices, cannot be summed at all, so you store their components (profit and revenue) and calculate the ratio at query time.
31
Write pandas code to calculate monthly revenue from an orders DataFrame.
A composite model mixes storage modes in one dataset: some tables in Import, some in DirectQuery, and some in Dual. For example, a huge transaction table stays in DirectQuery for freshness while small dimension tables are imported for speed. Aggregation tables can be imported so most visuals hit memory, with DirectQuery used only for detailed drill-down. The trade-off is more complexity and relationship rules to manage.
33
A stakeholder says the Power BI report shows different revenue than their Excel file. How do you troubleshoot?
I would first agree on definitions: gross or net, including tax or returns, order date or invoice date. Then compare one small slice, such as one month and one region, in both. I check filters on the report and page, relationships that drop unmatched rows, refresh time versus the Excel extract time, and duplicate rows. Once the gap is explained, I document the definition in the report so the question does not repeat.
34
On day 4 of a planned 14-day test, the product manager wants to stop because the dashboard shows treatment winning. What do you say?
I would explain that early results swing a lot and stopping on a good day inflates false positives, and that four days miss weekly patterns like weekend shopping. I would show how the lift has moved day by day and agree to the planned end date. If speed really matters, we can agree a sequential testing rule for future tests, but changing the rule mid-test after seeing data is not valid.
35
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.
36
After joining orders to payments, your revenue total doubles. What went wrong?
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.
VLOOKUP only looks to the right and breaks when columns are inserted. INDEX-MATCH looks in any direction and is robust. XLOOKUP is the modern option with exact match by default and left lookups.
38
Your Power BI dataset with five years of transactions takes two hours to refresh. How would you fix it?
Incremental refresh partitions a large table by date so that each refresh reloads only recent data, for example the last 7 days, while older partitions stay untouched. You create RangeStart and RangeEnd parameters, filter the table with them in Power Query, and set a policy such as store 5 years, refresh 7 days. Refresh becomes faster and lighter on the source. Query folding must work for the filter to be pushed to the database.
39
How would you design a sales dashboard for executives?
Start with KPIs (revenue, margin, growth), add trend and comparison to target, allow drill-down by region and product, keep it to one screen and test with the audience.
40
Write Python code to extract valid 10-digit Indian mobile numbers from a messy text column.
import re
pattern = re.compile(r'(?:\+?91[\s-]?|0)?([6-9]\d{9})')
def get_mobile(text):
if not isinstance(text, str):
return None
digits = re.sub(r'[\s()-]', '', text)
m = pattern.search(digits)
return m.group(1) if m else None
df['mobile'] = df['contact'].apply(get_mobile)
Tips to crack the State Bank of India Data Analytics interview
Follow official SBI recruitment notifications closely, as eligibility, exam pattern and stages change between recruitment cycles.
Prepare quantitative aptitude, reasoning, English and banking awareness thoroughly, practising full-length mock tests under exam timing.
For specialist IT officer roles, revise networking, databases, security and programming topics listed in the official syllabus.
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; State Bank of India interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the State Bank of India Data Analytics interview?
Candidates usually go through 4 stages: Preliminary online exam (for PO or clerk roles); Main online exam; Psychometric test, group exercise or interview; Document verification and final selection. 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 State Bank of India Data Analytics interview?
This practice set leans on Power BI (8), A/B Testing & Experiments (5), Data Cleaning (4), Tableau (4). Revise those first, then practise explaining one or two of your own projects end to end, because most State Bank of India interviewers ask you to walk through real work.
Are these the exact questions asked at State Bank of India?
No. These are curated practice questions that match the skills and difficulty usually tested in banking & finance interviews like State Bank of India's. They are not a leaked or verified list of past questions.
Where does State Bank of India hire Data Analytics professionals in India?
State Bank of India has large teams in Mumbai, Navi Mumbai. Openings and locations change often, so check the State Bank of India careers page and filter by city and experience.