Hexaware Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Hexaware hiring for Data Analytics roles
Hexaware Technologies is an Indian IT services company offering cloud, application, testing, data and business process services. It promotes an automation-first approach and serves clients in financial services, healthcare, travel and manufacturing. In India it hires for software engineers, test engineers, data analysts and data engineers.
For Data Analytics roles, this Hexaware practice set focuses most on Excel (9), SQL (6), Data Modelling & Warehousing (5), Dashboards and KPIs (4). It has 16 easy, 24 medium questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Navi Mumbai, India
Main India offices
Navi Mumbai, Chennai, Pune, Bengaluru, Noida
Hexaware Data Analytics interview process
Online assessment (aptitude + coding)
Communication assessment
Technical interview
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for HexawareStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
A simple structure is context, conflict, resolution. Context sets up what the audience already knows, such as our target was 10 percent growth. Conflict shows the surprising finding or problem, such as growth is only 3 percent because repeat buyers dropped. Resolution gives the recommendation and expected impact. Another option is situation, complication, question, answer. Either way, the audience should know the main message within the first minute.
02
What is the difference between loc and iloc in pandas?
loc selects by labels: row index labels and column names, for example df.loc[df['city'] == 'Pune', ['name', 'sales']]. iloc selects by integer position, for example df.iloc[0:5, 0:3] for the first five rows and three columns. With loc, slices include the end label; with iloc, the end position is excluded like normal Python. Use loc for readable filtering and iloc when position matters.
It automatically formats cells based on rules such as colour scales, data bars or highlighting values above a target, making patterns visible at a glance.
04
How would you find customers who never placed an order?
An A/B test compares one change with control. An A/B/n test compares several versions of one element, such as three headlines, with control. A multivariate test changes several elements at once, such as headline and button colour, and tests every combination to see each element's effect and their interactions. Multivariate tests need much more traffic because users are spread across many combinations.
06
What is bidirectional cross-filtering in Power BI and when is it risky?
Normally filters flow from the one side to the many side, for example from Product to Sales. Bidirectional filtering lets filters also flow back, so a Sales filter can limit Product. It helps in some many-to-many or bridge-table cases. The risks are ambiguous filter paths, unexpected results and slower performance. Prefer single direction and use CROSSFILTER inside a specific measure when you need both directions only there.
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.
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 ...).
10
Tell me about a time a dashboard you built changed a business decision.
Situation: our operations team assumed late deliveries were evenly spread. Task: I built a delivery performance dashboard. Action: I added a breakdown by courier partner and pin code cluster with a weekly trend, and reviewed it with the operations manager. Result: it showed one partner caused 60 percent of delays in two zones. The company shifted those zones to another partner, and on-time delivery rose from 84 to 92 percent in two months.
Readers scan from the top-left, so place the most important KPIs as cards across the top, then trend charts, then breakdowns and detail tables at the bottom. Group related visuals, align them on a grid and keep consistent spacing, fonts and colours. Put filters in one consistent place. Limit a page to roughly five to eight visuals that answer related questions, and give the page a clear title stating its purpose.
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
15
What are variables (VAR) in DAX and why are they useful?
VAR stores the result of an expression inside a measure, and RETURN gives the final output. Variables make measures readable, avoid evaluating the same expression twice, and are evaluated in the filter context where they are defined, which helps avoid subtle bugs inside CALCULATE. They also make debugging easy, because you can temporarily RETURN a variable to check its value before returning the final result.
16
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.
17
What is the difference between COUNT, COUNTA and COUNTIF?
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
19
During a review, an executive asks a question about your numbers that you cannot answer on the spot. What do you do?
Do not guess. Say clearly that you do not have that cut ready, restate the question to confirm it, and give a time by which you will reply, for example by end of day. If you know a partial answer or a reasonable expectation, share it with a caveat. Then follow up on time with a short, precise answer. Being honest and reliable builds more trust than a confident wrong number.
In GA4, an engaged session lasts longer than 10 seconds, has a conversion (key event) or has at least two page or screen views. Engagement rate is engaged sessions divided by total sessions. Bounce rate is simply the opposite: the share of sessions that were not engaged. This is more useful than the old single-page bounce definition, because a user who reads a full blog post for three minutes is no longer counted as a bounce.
23
What is a virtual environment in Python and why should analysts use one?
A virtual environment is an isolated folder with its own Python interpreter and installed packages, created with python -m venv or tools like conda or uv. Each project can then pin its own pandas or library versions without breaking other projects. Saving the package list in requirements.txt or a lock file lets a colleague or a scheduled server reproduce the same setup, which avoids the classic works-on-my-laptop problem.
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.
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.
27
What is the medallion architecture (bronze, silver, gold)?
Medallion architecture organises a lakehouse into three layers. Bronze holds raw data exactly as ingested, for audit and reprocessing. Silver holds cleaned, deduplicated, typed and joined data. Gold holds business-ready tables such as star schemas and aggregates used by dashboards. Each layer is built from the previous one by repeatable jobs, so if a cleaning rule changes you can rebuild silver and gold from bronze.
28
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.
29
What is a holdout group and why do teams keep one?
A holdout group is a small share of users, often 5-10%, kept out of a feature or of all marketing campaigns for a long time. Comparing them with everyone else shows the cumulative long-term impact, which individual short tests miss, and checks whether many small wins really add up. For example, a notification holdout shows whether push messages raise monthly retention or just shift activity earlier.
30
What is the LET function in Excel and why would you use it?
LET assigns names to intermediate results inside a single formula, for example =LET(sales, SUM(D2:D100), cost, SUM(E2:E100), (sales-cost)/sales). It makes long formulas readable and faster, because a repeated sub-calculation is evaluated once instead of several times. It is especially useful with dynamic arrays, where the same FILTER result might otherwise be recalculated three or four times in one nested formula.
31
Write SQL to generate a simple date dimension for 2026.
-- PostgreSQL syntax
CREATE TABLE dim_date AS
SELECT
TO_CHAR(d, 'YYYYMMDD')::INT AS date_key,
d::DATE AS full_date,
EXTRACT(YEAR FROM d) AS year,
EXTRACT(QUARTER FROM d) AS quarter,
EXTRACT(MONTH FROM d) AS month_no,
TO_CHAR(d, 'Mon') AS month_name,
TO_CHAR(d, 'Dy') AS weekday,
EXTRACT(ISODOW FROM d) IN (6, 7) AS is_weekend
FROM GENERATE_SERIES(DATE '2026-01-01', DATE '2026-12-31', INTERVAL '1 day') AS d;
32
Tell me about a time you found an error in someone else's spreadsheet.
Situation: a teammate's incentive calculation sheet was about to go to payroll. Task: I was asked to review it. Action: I noticed totals did not match the sales extract, traced it to a SUM range that stopped at row 500 while data had grown to 640 rows, and told the teammate privately with the fix. Result: we converted the data to an Excel Table so ranges expand, and payouts went out correctly.
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.
A tool that summarises large tables by dragging fields into rows, columns, values and filters, so you can slice sales by region or month without writing formulas.
37
What is the difference between DELETE, TRUNCATE and DROP?
This is very likely bot or spam traffic, or a misfiring tag. Check the user agents, IP ranges, hostnames, screen resolutions and landing pages for that traffic. If it is bots, exclude it with filters or data segments in analytics and in your reporting layer, and inform the web team so they can block it. Then restate the affected dashboard numbers, because bots inflate sessions and pull down conversion rate.
40
What is the difference between a table and a normal range?
A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.
Tips to crack the Hexaware Data Analytics interview
Practise aptitude and simple coding questions under time pressure for the online assessment.
Revise automation testing basics with Selenium and API testing, as quality engineering is an important service line.
Learn about Hexaware's automation and cloud focus and its key industry verticals 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; Hexaware interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Hexaware Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + coding); Communication assessment; Technical interview; 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 Hexaware Data Analytics interview?
This practice set leans on Excel (9), SQL (6), Data Modelling & Warehousing (5), Dashboards and KPIs (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Hexaware interviewers ask you to walk through real work.
Are these the exact questions asked at Hexaware?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Hexaware's. They are not a leaked or verified list of past questions.
Where does Hexaware hire Data Analytics professionals in India?
Hexaware has large teams in Navi Mumbai, Chennai, Pune, Bengaluru, Noida. Openings and locations change often, so check the Hexaware careers page and filter by city and experience.