Genpact Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Genpact hiring for Data Analytics roles
Genpact is a professional services company that began as a unit of General Electric in India. It provides business process management, finance and accounting operations, analytics, data and AI services. In India it hires for process associates, business analysts, data analysts, data engineers and data scientists.
For Data Analytics roles, this Genpact practice set focuses most on SQL (8), Statistics and Case Studies (8), Excel (5), Data Modelling & Warehousing (4). It has 20 easy, 20 medium questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
New York, USA
Main India offices
Gurugram, Hyderabad, Bengaluru, Noida, Kolkata
Genpact Data Analytics interview process
Online assessment (aptitude + English)
Technical or domain interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
How can a chart mislead even when the numbers are correct?
Common tricks are a truncated y-axis on bar charts, which exaggerates small differences; inconsistent scales across side-by-side charts; dual axes chosen to imply a correlation; cherry-picked time windows; cumulative charts that always rise; and area or 3D effects that distort size. Bars should start at zero, while line charts may zoom in if clearly labelled. Always check that the visual impression matches the actual magnitude.
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 ...).
In ETL, data is extracted from sources, transformed on a separate engine and then loaded into the warehouse. In ELT, raw data is loaded first and transformed inside the warehouse using SQL, often with a tool like dbt. Cloud warehouses with cheap storage and scalable compute made ELT the common choice in 2026, because raw data stays available for reprocessing. ETL still suits cases needing masking or heavy cleansing before data lands.
05
What questions do you ask a stakeholder before building a dashboard?
Who will use it and what decisions will they make with it? Which three to five questions must it answer? What are the KPI definitions, targets and comparison periods? Which data sources are trusted? How often will they look at it, and on what device? Who needs access and are there security restrictions? What does success look like in a month? I summarise the answers in a short brief and get sign-off before building.
06
What is method chaining in pandas and why is it useful?
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.
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month,
SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 1) AS mom_pct
FROM monthly
ORDER BY month;
A role-playing dimension is one physical dimension used several times by the same fact table in different roles. The classic case is the date dimension: an orders fact has order date, ship date and delivery date keys, all pointing to the same date table. In SQL you join it three times with different aliases; in Power BI you keep one active relationship and use USERELATIONSHIP or create separate date views for each role.
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.
11
How do you make a dashboard accessible, for example for colour-blind users?
Avoid red-green as the only difference; use colour-blind-safe palettes such as blue and orange, and add icons, labels or patterns so meaning never depends on colour alone. Ensure enough contrast between text and background, use readable font sizes, and add alt text to visuals. Keep a logical tab order for keyboard users, and offer a table view of chart data. Accessible dashboards are usually clearer for everyone.
12
Difference between a CTE, a subquery and a temp table?
A subquery is inline; a CTE is a named, readable block for one statement; a temp table is physically stored for the session and can be indexed and reused.
Dimensions are qualitative fields such as region, product or date that slice data into groups and create headers. Measures are quantitative fields such as sales or profit that get aggregated, by default with SUM. Dragging a dimension to rows creates one row per member; dragging a measure creates an axis. A numeric field like customer ID should be converted to a dimension so it is not summed.
pd.cut bins values into intervals you define or into equal-width ranges, for example age groups 18-25, 26-35 and 36-50. pd.qcut bins by quantiles so each bin has roughly the same number of rows, for example dividing customers into spend quartiles. Use cut when business-defined boundaries matter and qcut when you want balanced groups such as deciles. Both return a categorical column you can group by.
18
What is a snowflake schema and when would you choose it over a star schema?
A snowflake schema normalises dimensions into several related tables, for example product linked to subcategory linked to category, instead of keeping one flat product dimension. It saves some storage and avoids repeating attribute values, but needs more joins and is harder for users and BI tools. Most analytics teams prefer a star schema; snowflaking makes sense when a hierarchy is huge, shared by many dimensions or maintained by a separate team.
A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.
20
Your FILTER formula shows #SPILL! instead of results. Why, and how do you fix it?
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.
21
Numbers in an Excel column are stored as text and SUM returns zero. How do you fix it?
Signs are left-aligned numbers, green triangles, and COUNT returning less than COUNTA. Fix options: select the column and use the warning menu Convert to Number; use Data, Text to Columns and Finish; or a helper column with =VALUE(TRIM(A2)). If there are hidden characters, use =VALUE(SUBSTITUTE(TRIM(A2), CHAR(160), "")) to remove non-breaking spaces from web exports. In Power Query, simply change the column type to number.
Remove everything that does not help the message: heavy gridlines, borders, background colour, 3D effects, redundant legends and too many decimal places. Label data directly instead of using a legend, sort bars meaningfully, round numbers, and use one highlight colour with grey for the rest. Shorten axis labels and keep consistent fonts. Ask whether removing each element would lose information; if not, remove it.
23
What is the difference between a fact table and a dimension table?
A fact table stores measurable events at a defined grain, such as one row per order line, with numeric measures like quantity and amount plus foreign keys. Dimension tables describe the context of those events: customer, product, store and date, with descriptive attributes used for filtering and grouping. Facts are long and grow quickly; dimensions are wider and smaller. Together they form star or snowflake schemas.
24
Why does correlation not imply causation? Give a business example.
Two variables can move together because of a third factor, reverse causation or pure chance. Example: stores with more staff have higher sales, but busy locations get more staff because they already sell more, so hiring alone may not raise sales. Ice-cream sales and AC sales both rise in summer because of temperature. To claim causation you need experiments, natural experiments or careful control of confounders.
25
What does Flash Fill do in Excel, and when should you not rely on it?
Flash Fill (Ctrl+E) detects a pattern from one or two examples you type and fills the rest of the column, for example extracting first names or reformatting phone numbers. It is fast for one-time cleanup. However, it produces static values, not formulas, so it will not update when source data changes, and it can guess wrongly on irregular rows. For repeatable reports, use formulas or Power Query instead.
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.
A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis, usually no effect, were true. A p-value of 0.03 means that if the new page truly made no difference, results this strong would appear only about 3 percent of the time by chance. Below a chosen threshold such as 0.05 we reject the null. It is not the probability that the hypothesis is true.
29
What are named ranges in Excel and why are they useful?
A named range gives a cell or range a meaningful name, such as GST_Rate or SalesData, managed from the Name Manager. Formulas become readable, for example =Amount*GST_Rate instead of =B2*$H$1, and they do not break when you move the input cell. Names also work as data validation sources and in charts. For growing data, prefer Excel Tables, whose structured references expand automatically.
Variance is the average squared distance of values from their mean; standard deviation is its square root, in the same units as the data. They measure spread. Two stores may both average 50,000 rupees daily, but one with a standard deviation of 2,000 is stable while one with 20,000 is volatile. For samples, divide by n minus 1 instead of n to get an unbiased estimate.
CTR, click-through rate, is clicks divided by impressions. CPC, cost per click, is spend divided by clicks. CPA, cost per acquisition, is spend divided by conversions such as orders or leads. ROAS, return on ad spend, is revenue from ads divided by ad spend, so a ROAS of 4 means 4 rupees of revenue per rupee spent. ROAS ignores product margin, so a high ROAS can still lose money.
33
What is regression to the mean? Give a business example.
Regression to the mean means extreme results tend to be followed by more average results, simply because part of the extreme was luck. For example, if you pick the 10 worst-performing stores this quarter and give them a training programme, many will improve next quarter even without training. To claim the programme worked, compare them with similar low performers who did not get it.
34
What is the difference between a live connection and an extract in Tableau?
A live connection queries the source database every time a user interacts, so data is always current but performance depends on the database. An extract is a compressed snapshot stored in Tableau's hyper format, usually much faster, refreshed on a schedule and usable offline. Use live for near real-time needs on a fast warehouse, and extracts for speed, slow sources or when you want to reduce load on production systems.
35
What is the difference between a table and a normal range?
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.
38
Orders arrive in the warehouse with customer IDs that do not exist yet in the customer dimension. What do you do?
These are late-arriving dimensions. Do not drop the orders, or revenue will be understated. Common practice is to insert a placeholder customer row with the natural key and 'Unknown' attributes, link the order to it, and update that row when full details arrive. Alternatively map to a single Unknown member with key -1. Also monitor how many such rows appear daily, because a spike usually means a broken feed.
Sampling bias occurs when the sample does not represent the population, so conclusions do not generalise. Common types: selection bias, such as surveying only app users about website issues; survivorship bias, studying only customers who stayed; non-response bias, when unhappy customers skip surveys; and convenience sampling, using whoever is easiest to reach. Random sampling, stratification and weighting help reduce it.
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.
Tips to crack the Genpact Data Analytics interview
Learn process improvement basics like Lean and Six Sigma, which have long been part of Genpact's operating style.
Revise Excel, SQL and basic statistics for analytics roles, with examples of reporting or automation you built.
Read about Genpact's origins at GE and its finance and accounting outsourcing business for company questions.
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; Genpact interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Genpact Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + English); Technical or domain 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 Genpact Data Analytics interview?
This practice set leans on SQL (8), Statistics and Case Studies (8), Excel (5), Data Modelling & Warehousing (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Genpact interviewers ask you to walk through real work.
Are these the exact questions asked at Genpact?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Genpact's. They are not a leaked or verified list of past questions.
Where does Genpact hire Data Analytics professionals in India?
Genpact has large teams in Gurugram, Hyderabad, Bengaluru, Noida, Kolkata. Openings and locations change often, so check the Genpact careers page and filter by city and experience.