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Data Analytics Interview Questions in Chennai

Q: Which companies hire Data Analytics professionals in Chennai?

Companies with large offices in Chennai include TCS, Infosys, Wipro, Cognizant, Capgemini, Amazon, Cisco, Walmart, PayPal, Barclays. Use their company pages below to practise employer-specific sets.

Q: Are Data Analytics interviews in Chennai different from other cities?

The questions depend more on the company than the city. Chennai's mix of employers decides the style: Many companies here hire freshers in large batches and train them on the job

Q: How do I find Data Analytics jobs in Chennai?

Shortlist companies with Chennai offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Chennai filter, and practise the questions on this page before each interview.

36 practice questions with answers for Data Analytics interviews in Chennai.

Data Analytics jobs in Chennai

Chennai is a long-standing IT-services and SaaS hub along OMR, with strong demand for testing, data and support-engineering roles. Many companies here hire freshers in large batches and train them on the job.

Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Chennai.

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What is the difference between extract filters and data source filters in Tableau?

TableauMedium

Show answer

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.

When would you drop missing values, and when would you impute with mean or median?

Data CleaningMedium

Show answer

Drop rows when few are missing, roughly under 5 percent, and missingness looks random. Impute when dropping would lose too much data. Use the median for skewed numbers such as income or order value, the mean for roughly symmetric data, and the mode or an Unknown category for categoricals. Group-wise imputation, such as median by city, is better than one global value. Always add a flag column showing what was imputed.

A stakeholder asks for a pie chart of 12 product categories. When is a pie chart acceptable, and what would you suggest?

Data StorytellingEasy

Show answer

A pie chart works only for showing parts of a whole with very few categories, two to four, where one share clearly dominates, such as 70 percent of orders are prepaid. Avoid it when there are many slices, when values are close, or when comparing across periods, because people judge angles poorly. A sorted bar chart is almost always easier to read and compare. Never use 3D pies.

Write Python code to bootstrap a 95% confidence interval for the difference in revenue per user between two groups.

A/B Testing & ExperimentsHard

Show answer

import numpy as np rng = np.random.default_rng(42) control = df.loc[df.variant == 'A', 'revenue'].to_numpy() treat = df.loc[df.variant == 'B', 'revenue'].to_numpy() diffs = [] for _ in range(5000): c = rng.choice(control, size=len(control), replace=True) t = rng.choice(treat, size=len(treat), replace=True) diffs.append(t.mean() - c.mean()) low, high = np.percentile(diffs, [2.5, 97.5]) print(f'95% CI for difference: {low:.2f} to {high:.2f}')

How do you clean messy text data in Excel?

Data CleaningMedium

Show answer

Use TRIM, CLEAN, UPPER or PROPER, Text to Columns, Flash Fill, SUBSTITUTE, and Power Query for repeatable cleaning steps.

How do you make a dashboard accessible, for example for colour-blind users?

Dashboards and KPIsMedium

Show answer

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.

How do you improve Power BI report performance?

Power BIMedium

Show answer

Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.

A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?

Power BIMedium

Show answer

Totals in DAX are not the sum of visible rows; the measure is recalculated in the total's filter context. A measure like a distinct count, a ratio or an IF condition can give a different result at total level. If the business wants the sum of row values, iterate explicitly, for example SUMX ( VALUES ( Product[Name] ), [My Measure] ). Confirm with stakeholders which total is correct before changing it.

What is data profiling?

Data CleaningEasy

Show answer

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.

Which KPIs would you include in an HR attrition dashboard?

Dashboards and KPIsMedium

Show answer

Core KPIs: headcount, monthly and annualised attrition rate, voluntary versus involuntary split, and early attrition within the first 90 days or first year. Breakdowns by department, location, manager, tenure band, performance rating and salary band show where it concentrates. Add hiring metrics such as time to fill and offer acceptance rate, and exit reasons from exit interviews. Protect privacy by hiding very small groups.

Write SQL to get each order's customer city as it was on the order date, using an SCD Type 2 customer dimension.

Data Modelling & WarehousingHard

Show answer

-- dim_customer has valid_from, valid_to (NULL for current row) SELECT o.order_id, o.order_date, c.customer_id, c.city FROM fact_orders o JOIN dim_customer c ON c.customer_id = o.customer_id AND o.order_date >= c.valid_from AND o.order_date < COALESCE(c.valid_to, DATE '9999-12-31');

Find the second highest salary in SQL.

SQLEasy

Show answer

SELECT MAX(salary) FROM emp WHERE salary < (SELECT MAX(salary) FROM emp); -- or: DENSE_RANK() OVER (ORDER BY salary DESC) = 2

What is the difference between SUM and SUMX in DAX?

Power BIMedium

Show answer

SUM adds up a single column in the current filter context. SUMX is an iterator: it goes row by row over a table, evaluates an expression for each row, then sums the results. Use SUMX when the value must be computed per row first, for example SUMX(Sales, Sales[Qty] * Sales[Price]). Iterators are flexible but can be slower on very large tables, so storing a simple column is sometimes better.

Write pandas code to find the top 2 products by revenue in each region.

Python for AnalysisMedium

Show answer

top2 = ( df.groupby(['region', 'product'], as_index=False)['revenue'] .sum() .sort_values(['region', 'revenue'], ascending=[True, False]) .groupby('region') .head(2) ) print(top2)

Tell me about a time you defined a new metric for a team.

Dashboards and KPIsMedium

Show answer

Situation: the support team was judged only on tickets closed, which encouraged rushed replies. Task: propose a better measure. Action: I analysed reopen rates and satisfaction scores, then proposed first-contact resolution rate with clear rules for what counts as resolved. I tested it on three months of history and reviewed edge cases with team leads. Result: it became the main KPI and reopen rates fell 18% in a quarter.

What are leading and lagging indicators? Give examples.

Dashboards and KPIsMedium

Show answer

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.

What is a transaction and what do the ACID properties mean?

SQLMedium

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A transaction is a group of SQL statements that succeed or fail together, such as debiting one account and crediting another. ACID: Atomicity means all or nothing; Consistency means rules and constraints stay valid; Isolation means concurrent transactions do not see each other's partial work; Durability means committed data survives crashes. Analysts mostly read data, but knowing ACID explains why reports should read committed data.

Your NOT IN query unexpectedly returns zero rows. Why might that happen, and how do EXISTS and IN differ?

SQLMedium

Show answer

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.

What is the difference between an outlier and a data error?

Data CleaningMedium

Show answer

An outlier is a genuine but unusual value, such as a bulk corporate order of 50 laptops; it is real and may be important. A data error is an incorrect value, such as an age of 250 or a price entered in paise instead of rupees. Errors should be fixed or removed. Outliers should be investigated and often kept, perhaps analysed separately or with median-based metrics so they do not distort averages.

What are field parameters in Power BI?

Power BIMedium

Show answer

Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.

How do you present insights to non-technical stakeholders?

Data StorytellingMedium

Show answer

Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.

What KPIs would you track for a customer support call centre?

Dashboards and KPIsMedium

Show answer

Volume and staffing: calls offered, answered, abandoned rate and average speed of answer. Efficiency: average handle time and occupancy. Quality and outcome: first contact resolution, customer satisfaction or CSAT score, and repeat contacts within 7 days. Service level, for example 80 percent of calls answered in 20 seconds, ties them together. Balance speed metrics with quality, otherwise agents rush calls to cut handle time.

What is the difference between loc and iloc in pandas?

Python for AnalysisEasy

Show answer

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.

Many reporting queries in your team use SELECT DISTINCT to remove duplicate rows. What is the risk?

SQLMedium

Show answer

SELECT DISTINCT often hides a real problem, such as a join on the wrong key or a one-to-many join that multiplies rows. The output looks clean, but sums computed before the DISTINCT, or legitimate repeated rows, can be wrong, and DISTINCT adds a costly sort on large tables. Instead, find why duplicates appear, fix the join or aggregate the child table first, and add a uniqueness test on the key.

What are novelty and primacy effects in experiments?

A/B Testing & ExperimentsEasy

Show answer

A novelty effect is when users engage more with a change simply because it is new, so the early lift fades over time. A primacy effect is the opposite: existing users resist a change at first because they are used to the old design, so results start poor and improve. Both mean short tests mislead. Run tests for at least one or two full weeks and compare new versus returning users.

What is statistical significance vs practical significance?

Statistics and Case StudiesMedium

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A result can be statistically significant yet too small to matter; always report effect size and business impact together with the p-value.

What does CASE WHEN do?

SQLMedium

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It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.

What is a role-playing dimension?

Data Modelling & WarehousingMedium

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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.

What is a gateway in Power BI?

Power BIMedium

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A bridge that lets the Service refresh data from on-premises sources securely on a schedule.

A stakeholder wants 20 more visuals added to an already crowded dashboard page. How do you respond?

Dashboards and KPIsMedium

Show answer

I would ask which decisions each new visual supports and how often they will look at it. Usually a few are essential and the rest are nice to have. I would propose keeping the main page focused on core KPIs and moving detail into drill-through pages, tooltips or a separate analysis tab. Showing a quick mock-up of both versions helps them see that the cleaner page answers questions faster.

Tell me about a time you had to present findings when the data was incomplete.

Data StorytellingMedium

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Situation: leadership wanted the impact of a new store format after three weeks, but two stores had missing POS data. Task: give a useful answer on time. Action: I presented results for the complete stores, showed the range of outcomes if missing stores performed like the best and worst cases, and clearly marked what was unknown. Result: they approved a limited extension, and the final four-week analysis confirmed the early direction.

What is a primary key vs a foreign key?

SQLEasy

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A primary key uniquely identifies each row in its table; a foreign key references another table's primary key to enforce relationships.

Tell me about a time tracking broke and you had to fix the reporting.

Web & Marketing AnalyticsMedium

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Situation: after a checkout redesign, purchases in our analytics fell 35% overnight while orders were normal. Task: find the cause and protect the weekly report. Action: I compared analytics with the order table by page and device, found the purchase tag no longer fired on the new UPI payment success page, and worked with developers to fix it. Result: I backfilled the gap from order data, added a footnote and set up a daily mismatch alert.

Explain correlation vs regression.

Statistics and Case StudiesMedium

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Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.

What is a north star metric?

Dashboards and KPIsEasy

Show answer

A north star metric is the single measure that best captures the value a product delivers to customers and predicts long-term growth, such as weekly active learners for an education app or orders delivered for a delivery service. It aligns teams around one outcome. It should be supported by input metrics that teams can influence, such as activation rate and repeat usage, and balanced with guardrails like cost or quality.

When do you use a chi-square test?

Statistics and Case StudiesMedium

Show answer

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.

Companies with offices in Chennai (44)

IT services

TCS

Data Analytics interview questions

40 questions
4 rounds

Mumbai, Chennai, Pune +2

Data Analytics Interview Questions in Chennai

Data Analytics jobs in Chennai

What is the difference between extract filters and data source filters in Tableau?

When would you drop missing values, and when would you impute with mean or median?

A stakeholder asks for a pie chart of 12 product categories. When is a pie chart acceptable, and what would you suggest?

Write Python code to bootstrap a 95% confidence interval for the difference in revenue per user between two groups.

How do you clean messy text data in Excel?

How do you make a dashboard accessible, for example for colour-blind users?

How do you improve Power BI report performance?

A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?

What is data profiling?

Which KPIs would you include in an HR attrition dashboard?

Write SQL to get each order's customer city as it was on the order date, using an SCD Type 2 customer dimension.

Find the second highest salary in SQL.

What is the difference between SUM and SUMX in DAX?

Write pandas code to find the top 2 products by revenue in each region.

Tell me about a time you defined a new metric for a team.

What are leading and lagging indicators? Give examples.

What is a transaction and what do the ACID properties mean?

Your NOT IN query unexpectedly returns zero rows. Why might that happen, and how do EXISTS and IN differ?

What is the difference between an outlier and a data error?

What are field parameters in Power BI?

How do you present insights to non-technical stakeholders?

What KPIs would you track for a customer support call centre?

What is the difference between loc and iloc in pandas?

Many reporting queries in your team use SELECT DISTINCT to remove duplicate rows. What is the risk?

What are novelty and primacy effects in experiments?

What is statistical significance vs practical significance?

What does CASE WHEN do?

What is a role-playing dimension?

What is a gateway in Power BI?

A stakeholder wants 20 more visuals added to an already crowded dashboard page. How do you respond?

Tell me about a time you had to present findings when the data was incomplete.

What is a primary key vs a foreign key?

Tell me about a time tracking broke and you had to fix the reporting.

Explain correlation vs regression.

What is a north star metric?

When do you use a chi-square test?

Companies with offices in Chennai (44)

TCS

Infosys

Wipro

Cognizant

Capgemini

Amazon

Cisco

Walmart

PayPal

Barclays

HCLTech

Tech Mahindra

Mphasis

LTIMindtree

Hexaware

Birlasoft

EPAM Systems

Thoughtworks

Virtusa

UST

CGI

NTT DATA

LatentView Analytics

Tiger Analytics

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