Virtusa Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Virtusa hiring for Data Analytics roles
Virtusa is a digital engineering and IT services company providing application development, testing, cloud and data services. It has a strong client base in banking, financial services, healthcare and telecom. Its India centres in Hyderabad, Chennai and Bengaluru hire for software engineers, test engineers and data professionals.
For Data Analytics roles, this Virtusa practice set focuses most on Power BI (5), Statistics and Case Studies (5), Tableau (4), Web & Marketing Analytics (4). It has 14 easy, 26 medium questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Southborough, Massachusetts, USA
Main India offices
Hyderabad, Chennai, Bengaluru
Virtusa Data Analytics interview process
Online assessment (aptitude + reasoning)
Coding test
Technical interview
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for VirtusaStart 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.
✓ 3 questions free • Unlock the rest in seconds
01
Write a Tableau calculated field that keeps only sales from the last 30 days.
// 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
02
What is the difference between a dashboard and a report?
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.
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.
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.
06
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;
07
Write pandas code to calculate customer acquisition cost and payback period in months for each channel.
A degenerate dimension is a dimension key stored directly in the fact table with no separate dimension table, because it has no other attributes worth storing. Invoice number, order number and transaction ID are typical examples. They are useful for grouping order lines back into an order, counting distinct orders or tracing a row to the source system. Creating a separate table just for the number would add a useless join.
11
Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).
-- C2 = invoice date, TODAY() = as-of date
=LET(age, TODAY()-C2,
IFS(age<=30, "0-30",
age<=60, "31-60",
age<=90, "61-90",
TRUE, "90+"))
-- then a PivotTable with bucket in rows and SUM of amount gives the ageing report
12
What is the difference between users, sessions and events in Google Analytics 4?
GA4 uses an event-based model: every interaction, such as page_view, scroll, add_to_cart or purchase, is an event with parameters. A session is a group of events from one user within a period of activity, ending after 30 minutes of inactivity by default. A user is identified by a device cookie, app instance or a logged-in user ID. One user can have many sessions, and each session many events.
13
Pin codes in your CSV lost their leading zeros after opening in Excel. Why and how do you prevent it?
Excel guessed the column was a number and dropped leading zeros; the same happens with long IDs turning into scientific notation. Identifiers that are not used in arithmetic should be stored as text. Import through Data, From Text/CSV or Power Query and set the column type to Text, or read it in pandas with dtype={'pincode': str}. To repair, use =TEXT(A2, "000000") when the original length is known.
14
When would you use a one-tailed test instead of a two-tailed test?
A two-tailed test checks for a difference in either direction and is the safer default for A/B tests. A one-tailed test checks only one direction, such as treatment being better, and needs fewer users. Use it only if you decided beforehand that you would act the same way whether the change has no effect or a negative effect. Otherwise you may miss harm, which is why most teams stay two-tailed.
15
Your NOT IN query unexpectedly returns zero rows. Why might that happen, and how do EXISTS and IN differ?
IN checks whether a value is in a list or subquery result; EXISTS checks whether a subquery returns at least one row and stops at the first match. Results are the same in most cases, and modern optimisers often run them similarly. The big difference is NOT IN with NULLs: if the subquery returns any NULL, NOT IN returns no rows. NOT EXISTS behaves correctly, so prefer it for anti-joins.
16
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.
17
What is a percentile and what is the interquartile range?
A percentile is the value below which a given percentage of data falls; the 90th percentile delivery time means 90 percent of deliveries were faster. The interquartile range is the 75th percentile minus the 25th percentile, the spread of the middle half. IQR is robust to outliers, and values beyond 1.5 times the IQR from the quartiles are often flagged as outliers in box plots.
18
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.
19
Write a DAX measure for a KPI card showing achievement against target and a colour for conditional formatting.
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
20
Why would you publish a Tableau data source separately instead of embedding it in each workbook?
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.
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.
22
What is an action title and why should you use one?
An action title states the insight of a chart or slide as a sentence instead of a label. Instead of Monthly Sales by Region, write South region drove 70 percent of the decline since July. Busy executives may read only the titles, so each one should carry the message and, ideally, the so-what. The chart below then proves the title. Read only your titles in order: they should tell the whole story.
A story is a sequence of sheets or dashboards, called story points, that walk an audience through an analysis step by step, like slides. Each point can keep its own filters and selections and has a caption explaining the insight. It is useful for presenting findings such as why churn rose, moving from the headline number to segments and recommendations. For everyday monitoring, a regular dashboard is better than a story.
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
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.
29
Your findings deck has 30 slides and you have 10 minutes with leadership. How do you cut it down?
Write the single key message first, then keep only slides that directly support it, usually three to five: the headline finding, the main driver, the impact in money, and the recommendation with next steps. Merge related charts, use action titles, and move everything else to an appendix for questions. Rehearse to fit in about seven minutes, leaving time for discussion, because the discussion is where decisions are made.
30
How do you calculate working days between two dates in Excel, excluding holidays?
-- A2 = start date, B2 = end date, Holidays = named range of holiday dates
=NETWORKDAYS(A2, B2, Holidays)
-- if the weekend is only Sunday (weekend code 11)
=NETWORKDAYS.INTL(A2, B2, 11, Holidays)
-- date that is 10 working days after A2
=WORKDAY(A2, 10, Holidays)
31
Tell me about a time you had to learn a new tool or library quickly for a project.
Situation: in my internship the team needed survey results analysed in Python, which I had only used in coursework. Task: deliver cleaned data and charts within a week. Action: I spent two evenings on pandas and Seaborn tutorials using our real file, kept a notes document of commands, and asked a senior to review my first notebook. Result: I delivered on day five, and my notebook became the template for the next survey.
32
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.
33
What is the difference between mean and median for reporting salaries?
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.
35
What is the difference between a t-test and a z-test?
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.
A pivot table summarises large data by dragging fields into rows, columns, values and filters — for example total sales by region and month — without writing formulas.
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.
38
A profit margin visual shows errors for products with zero sales. How do you fix it in DAX?
DIVIDE(numerator, denominator, alternate) handles division by zero or blank safely and returns blank or the alternate value you supply, so visuals do not show errors or infinity. The / operator returns an error or infinity in those cases. For example, DIVIDE([Profit], [Sales]) shows blank for a product with no sales. Use / only when you are certain the denominator can never be zero, because it is marginally faster.
Import loads data into memory for fast visuals; DirectQuery queries the source live, giving fresh data but slower performance and some DAX limits.
Tips to crack the Virtusa Data Analytics interview
Revise banking and healthcare domain basics, which are important industries in Virtusa's client portfolio.
Practise coding questions and SQL queries for both the online test and technical interview.
Learn about its engineering-first positioning and be ready to describe how you approach learning new tools.
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; Virtusa interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Virtusa Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Coding test; 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 Virtusa Data Analytics interview?
This practice set leans on Power BI (5), Statistics and Case Studies (5), Tableau (4), Web & Marketing Analytics (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Virtusa interviewers ask you to walk through real work.
Are these the exact questions asked at Virtusa?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Virtusa's. They are not a leaked or verified list of past questions.
Where does Virtusa hire Data Analytics professionals in India?
Virtusa has large teams in Hyderabad, Chennai, Bengaluru. Openings and locations change often, so check the Virtusa careers page and filter by city and experience.