NTT DATA Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About NTT DATA hiring for Data Analytics roles
NTT DATA is a global IT services company and part of the NTT Group of Japan. It provides consulting, application services, cloud, infrastructure, data and testing services for clients in many industries. Its India delivery centres hire for software engineers, test engineers, data engineers, data analysts and infrastructure professionals.
For Data Analytics roles, this NTT DATA practice set focuses most on Statistics and Case Studies (6), Excel (6), Dashboards and KPIs (6), A/B Testing & Experiments (4). It has 18 easy, 22 medium questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Tokyo, Japan
Main India offices
Bengaluru, Pune, Noida, Hyderabad, Chennai
NTT DATA Data Analytics interview process
Online assessment (aptitude + technical)
Technical 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 would you write a short email summarising an analysis for a manager?
Use a clear subject that states the finding, such as Q3 repeat purchase rate fell 4 points, mainly in Tier 2 cities. Open with one or two sentences of the key insight and the recommendation. Follow with three bullet-style lines of supporting evidence with numbers, then any caveats about data, and a clear next step or decision needed with a date. Attach or link the detailed workings instead of pasting large tables.
02
What are primary metrics and guardrail metrics in an A/B test?
The primary metric is the one decision metric the test is designed to move, such as checkout conversion. Guardrail metrics are ones that must not get worse, such as page load time, refund rate, unsubscribe rate or revenue per user. A change that lifts conversion but increases cancellations should not ship. Define both before launch, so the decision rule is clear and nobody picks the favourable metric afterwards.
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.
04
Write Excel formulas to get a sorted list of unique customer names and their count.
-- unique customers from column B, sorted A to Z
=SORT(UNIQUE(B2:B1000))
-- number of unique customers
=COUNTA(UNIQUE(B2:B1000))
-- unique customers sorted by total sales, highest first
=SORTBY(UNIQUE(B2:B1000), SUMIFS(D2:D1000, B2:B1000, UNIQUE(B2:B1000)), -1)
05
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.
06
What are leading and lagging indicators? Give examples.
Lagging indicators report outcomes that have already happened, such as monthly revenue, profit or churn; they are accurate but too late to change. Leading indicators move earlier and predict outcomes, such as qualified leads, demo bookings, cart additions or support ticket backlog. A sales dashboard should pair them: pipeline coverage today signals revenue next quarter. Leading indicators need validation that they really predict the lagging result.
07
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.
08
What is the difference between COUNT, COUNTA and COUNTIF?
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.
Data profiling is a quick systematic look at a dataset before analysis: row counts, data types, null percentages, distinct counts, minimum and maximum, common values and value patterns per column. In pandas, df.info(), df.describe(include='all') and value_counts() cover most of it; Power Query has column quality and distribution views. Profiling reveals problems like a date column stored as text or a status field with unexpected values early.
12
What is an A/B test and why is random assignment so important?
An A/B test is a controlled experiment where users are split into a control group that sees the current version and a treatment group that sees a change, and you compare a metric such as conversion. Random assignment makes the groups similar in every way except the change, including things you cannot measure. That is why a difference in results can be attributed to the change itself, not to user mix or seasonality.
Profile the data, remove exact duplicates using keys, impute missing values (mean/median/mode or a business rule), flag imputed rows and document every cleaning step.
A data mart is a subset of the warehouse built for one department or subject, such as sales, finance or HR. It contains the facts and dimensions that team needs, often pre-aggregated and with business-friendly names. Marts make reporting faster and simpler for that team. Ideally they are built from the central warehouse using conformed dimensions, so the finance mart and the sales mart still agree on revenue.
Keep a cleaning log or README listing each rule, why it exists and its impact, for example removed 1,240 test orders flagged by email domain. Code-based cleaning in SQL, Python or Power Query steps is itself documentation if steps are named clearly. Record counts before and after each major step. This lets reviewers trust the numbers, helps a colleague rerun the work, and answers the question why your total differs from the raw file.
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.
Tableau is a visual analytics tool for building interactive charts and dashboards by dragging fields onto shelves. Tableau Desktop is used to author workbooks, Tableau Prep cleans and shapes data, Tableau Server and Tableau Cloud host and share dashboards with security and scheduled refresh, and Tableau Public is a free version where work is saved publicly. Analysts usually build in Desktop and publish to Server or Cloud.
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.
23
Your test increased conversion rate by 3% but average order value dropped by 5%. What do you recommend?
Look at the metric that reflects business value, such as revenue or margin per visitor, which combines both effects. Here revenue per visitor is roughly 1.03 times 0.95, about 2% lower, so the change may hurt. Check whether the drop is significant, which segments drive it, and whether new buyers are making smaller first orders that grow later. Without a strong reason, I would not ship it as is.
24
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.
You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.
order_date is probably a DATETIME or TIMESTAMP column, so the filter matches only rows at exactly midnight, 2026-03-31 00:00:00. Use a half-open range instead: WHERE order_date >= '2026-03-31' AND order_date < '2026-04-01'. This keeps the index usable, unlike wrapping the column in CAST or DATE(), and it correctly includes orders at 23:59:59.500. Also check the time zone of the stored timestamps.
Power BI Desktop is the free Windows application where you connect data, model it, write DAX and design reports. Power BI Service is the cloud platform where reports are published, shared, scheduled for refresh, and organised into workspaces and apps. Power BI Report Server is an on-premises option for organisations that cannot use the cloud, with fewer features. Most analysts build in Desktop and share through the Service.
30
What is the difference between a table and a normal range?
A ratio like conversion rate or average order value can move because the numerator or the denominator changed, so always show both components nearby. Ratios can look dramatic on small bases, such as 2 of 4 users. They also cannot be averaged across segments; you must divide total numerator by total denominator, otherwise small segments get too much weight. Counts are simpler but need context like trends or targets.
32
How do you show actual versus target on a dashboard?
Use a KPI card with actual, target and variance as both value and percentage, coloured by status. For trends, a bar or line for actual with a target line or band works well. Bullet charts compare actual against target and ranges compactly in tables. Make the period clear, such as month to date versus prorated monthly target, because comparing half a month of sales to a full month target looks falsely bad.
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.
35
What is RAG status and how should thresholds be set?
RAG means red, amber, green, a traffic-light status that shows at a glance whether a KPI is on track. Thresholds should be agreed with the business owner in advance, for example green at or above 100 percent of target, amber from 90 to 99 percent, red below 90 percent. Do not rely only on colour; add icons or text for colour-blind users, and avoid so many reds that nobody reacts.
36
Write an Excel formula to look up a price using two criteria, product and city.
=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
37
How does the FILTER function work in Excel? Write a formula to list all North region orders.
-- A2:D500 holds Order, Customer, Region, Amount
=FILTER(A2:D500, C2:C500="North", "No orders")
-- two conditions (AND) multiply the tests
=FILTER(A2:D500, (C2:C500="North")*(D2:D500>10000), "No orders")
-- OR conditions add the tests
=FILTER(A2:D500, (C2:C500="North")+(C2:C500="East"))
ROW_NUMBER gives a unique sequence. RANK gives ties the same rank and skips the next (1, 1, 3). DENSE_RANK gives ties the same rank without gaps (1, 1, 2).
39
Write Python code to calculate the sample size per group for a conversion test with a 4% baseline and a 10% relative lift.
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.
Tips to crack the NTT DATA Data Analytics interview
Revise SQL, testing and programming fundamentals for both the online assessment and technical interview.
Learn about NTT DATA's Japanese parent and its global integration of companies such as Dimension Data.
Be ready to discuss working with clients across time zones, as many teams support global accounts.
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; NTT DATA interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the NTT DATA Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); 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 NTT DATA Data Analytics interview?
This practice set leans on Statistics and Case Studies (6), Excel (6), Dashboards and KPIs (6), A/B Testing & Experiments (4). Revise those first, then practise explaining one or two of your own projects end to end, because most NTT DATA interviewers ask you to walk through real work.
Are these the exact questions asked at NTT DATA?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like NTT DATA's. They are not a leaked or verified list of past questions.
Where does NTT DATA hire Data Analytics professionals in India?
NTT DATA has large teams in Bengaluru, Pune, Noida, Hyderabad, Chennai. Openings and locations change often, so check the NTT DATA careers page and filter by city and experience.