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Data Analytics Technical Round Questions & Answers

40 questions for the technical round stage — 3 easy, 31 medium, 6 hard.

About the technical round round

The technical round is the core of a Data Analytics interview: concept questions with follow-ups, a live hands-on task and a deep dive into one of your projects. Interviewers check depth, not just definitions.

Tips for the technical round round

  • Say your approach out loud before you start writing.
  • After a correct answer, expect 'why?' and 'what if?' follow-ups; go one level deeper than the question.
  • Pick one project you can explain end to end, including what you would do differently.
  • If you get stuck, explain what you would try next instead of going silent.
Solved — technical roundStart practicing
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01

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

Data CleaningMedium
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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.
02

How do you present insights to non-technical stakeholders?

Data StorytellingMedium
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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.
03

Write a Tableau calculation to find each customer's first purchase date and flag new versus repeat orders.

TableauMedium
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// First Purchase Date { FIXED [Customer ID] : MIN([Order Date]) } // Order Type IF [Order Date] = [First Purchase Date] THEN 'New' ELSE 'Repeat' END // Cohort Month for retention analysis DATETRUNC('month', [First Purchase Date])
04

How would you write a short email summarising an analysis for a manager?

Data StorytellingMedium
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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.
05

Sales dropped 20% last month. How will you analyse it?

Statistics and Case StudiesMedium
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Confirm data accuracy first → break down by region, product, channel and customer segment → compare with seasonality and last year → check pricing, stock-outs, marketing changes and funnel conversion → present the root causes with clear charts.
06

What is the unit of randomisation in an experiment, and how do you choose it?

A/B Testing & ExperimentsMedium
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The unit of randomisation is what gets assigned to control or treatment: a user, a session, a device, a store or a city. Usually it should be the user, so one person always sees the same version. Session-level randomisation can show one user both versions and contaminate results. Your analysis unit should match it; if you randomise by user but analyse per page view, standard errors become too small.
07

What is the difference between a table and a normal range?

ExcelMedium
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A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.
08

Your pandas script shows SettingWithCopyWarning and a new column does not update. What is happening and how do you fix it?

Python for AnalysisMedium
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It warns that you may be modifying a temporary copy of a slice, so the change might not reach the original DataFrame, for example df[df.sales > 0]['flag'] = 1. Avoid it by using a single loc call, df.loc[df.sales > 0, 'flag'] = 1, or by calling .copy() when you intentionally create a new subset. Newer pandas versions with copy-on-write make behaviour more predictable, but clear loc assignments remain best practice.
09

DAX: calculated column vs measure?

Power BIMedium
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A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.
10

How do you create a dynamic dashboard in Excel?

ExcelMedium
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Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
11

What are field parameters in Power BI?

Power BIMedium
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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.
12

What is the difference between sets and groups in Tableau?

TableauMedium
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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.
13

What is the difference between COUNT(*), COUNT(column) and COUNT(DISTINCT column)?

SQLEasy
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COUNT(*) counts all rows, including rows with NULLs. COUNT(column) counts only rows where that column is not NULL. COUNT(DISTINCT column) counts unique non-null values. For example, in an orders table, COUNT(*) gives number of orders, COUNT(coupon_code) gives orders that used a coupon, and COUNT(DISTINCT customer_id) gives number of unique buyers. Mixing these up is a common source of wrong KPIs.
14

What is vectorisation and why does it matter?

Python for AnalysisMedium
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Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.
15

Write an Excel formula to look up a price using two criteria, product and city.

ExcelMedium
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=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
16

Which KPIs would you track for an e-commerce app?

Dashboards and KPIsMedium
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DAU/MAU, conversion rate, average order value, cart abandonment, retention/churn, CAC, LTV, return rate and NPS.
17

What is the grain of a fact table and why should you decide it first?

Data Modelling & WarehousingMedium
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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.
18

How do you create a new column from conditions?

Python for AnalysisMedium
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Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.
19

What is the difference between ALL, ALLEXCEPT and REMOVEFILTERS in DAX?

Power BIHard
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ALL removes filters from a table or columns and can also return all rows as a table. REMOVEFILTERS only removes filters and is clearer inside CALCULATE. ALLEXCEPT removes all filters on a table except the columns you list. A common use is share of total: DIVIDE([Sales], CALCULATE([Sales], ALL(Product))) gives each product's percentage, while ALLEXCEPT(Product, Product[Category]) gives share within its category.
20

Write SQL for a running total of sales by date.

SQLMedium
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SELECT order_date, SUM(amount) OVER (ORDER BY order_date) AS running_total FROM sales;
21

You launched a dashboard a month ago and usage logs show almost nobody opens it. What do you do?

Dashboards and KPIsMedium
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Talk to the intended users before redesigning. Find out whether they know it exists, whether it answers their real questions, whether they trust the numbers, and whether it fits their routine. Common fixes are fewer, sharper KPIs, faster loading, a scheduled email snapshot, or embedding it in a meeting that already happens. Then measure usage again. Sometimes the honest answer is that the dashboard is not needed and can be retired.
22

Write SQL to group raw page events into sessions using a 30-minute inactivity rule.

Web & Marketing AnalyticsHard
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WITH ordered AS ( SELECT user_id, event_ts, LAG(event_ts) OVER (PARTITION BY user_id ORDER BY event_ts) AS prev_ts FROM page_events ), flagged AS ( SELECT *, CASE WHEN prev_ts IS NULL OR event_ts - prev_ts > INTERVAL '30 minutes' THEN 1 ELSE 0 END AS new_session FROM ordered ) SELECT user_id, event_ts, SUM(new_session) OVER (PARTITION BY user_id ORDER BY event_ts) AS session_no FROM flagged;
23

What is a CTE and when would you use one?

SQLMedium
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A Common Table Expression (WITH name AS (...)) is a named temporary result used inside one query. Use it to break complex logic into readable steps or for recursive queries.
24

What is normalisation?

SQLMedium
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Organising tables to reduce redundancy and anomalies by splitting data into related tables, commonly up to third normal form.
25

What do the match_mode and search_mode arguments of XLOOKUP control?

ExcelMedium
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match_mode decides how a value is matched: 0 exact (default), -1 exact or next smaller, 1 exact or next larger, and 2 wildcard. search_mode decides direction: 1 searches first to last, -1 searches last to first, and 2 or -2 use binary search on sorted data. Searching last to first is a quick way to fetch the latest entry for a customer without sorting, and next-smaller matching handles tax slabs or commission bands.
26

What is the difference between melt and pivot?

Python for AnalysisMedium
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melt converts wide data to long format; pivot converts long data to wide, and they are used to reshape data for analysis or charts.
27

Write SQL to calculate daily active users and the DAU/MAU stickiness ratio.

Dashboards and KPIsHard
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WITH dau AS ( SELECT CAST(event_ts AS DATE) AS day, COUNT(DISTINCT user_id) AS dau FROM events GROUP BY CAST(event_ts AS DATE) ) SELECT d.day, d.dau, (SELECT COUNT(DISTINCT e.user_id) FROM events e WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day) AS mau, ROUND(100.0 * d.dau / (SELECT COUNT(DISTINCT e.user_id) FROM events e WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day), 1) AS stickiness_pct FROM dau d ORDER BY d.day;
28

Sales data from different countries mixes currencies and some weights are in grams while others are in kilograms. How do you handle it?

Data CleaningHard
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Never add mixed units. Identify the unit for each row from a currency or unit column, or infer it from the source system. Convert to one standard, for example INR using a dated exchange rate table joined on transaction date, and kilograms using a conversion factor. Keep the original value and unit columns for audit. Document which rates were used, and flag rows where the unit could not be determined.
29

Your Tableau map shows a large number of unknown locations. How do you fix it?

TableauEasy
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Click the unknown indicator at the bottom right of the map and choose Edit Locations. Usually Tableau cannot match names because of spelling variants, such as 'Bengaluru' versus 'Bangalore', or an ambiguous city that needs the state and country set. Make sure fields have the correct geographic role, add a country or state field to the view, clean names in the data source, or use latitude and longitude columns.
30

How can you automate data quality checks in a reporting pipeline?

Data CleaningHard
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Define tests as code that run after every load: not-null and unique checks on keys, accepted value lists, referential integrity, row count within an expected range compared with recent days, and freshness of the latest timestamp. Store results in a table and alert the team on failures before dashboards refresh. Start with checks on the few tables behind key KPIs, then expand. This catches issues before stakeholders do.
31

Why would you publish a Tableau data source separately instead of embedding it in each workbook?

TableauMedium
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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.
32

What is method chaining in pandas and why is it useful?

Python for AnalysisMedium
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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.
33

What are table calculations in Tableau?

TableauMedium
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Table calculations are computed on the aggregated results already in the view, not on the underlying rows. Examples are running total, percent of total, difference from previous, rank and moving average. Their result depends on the direction and partition you set with Compute Using, for example table across or by specific dimensions. They are quick to add from Quick Table Calculation, but incorrect Compute Using settings are a frequent cause of wrong numbers.
34

What steps would you apply in Power Query to clean a messy system export?

Data CleaningMedium
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Typical steps: remove top junk rows and promote the real header row, remove blank rows and columns, set correct data types, trim and clean text, split combined columns such as Name - City, replace error values, fill down for grouped labels, remove duplicates on the business key, and unpivot wide month columns. Rename steps meaningfully so others can follow them, and keep the query foldable when the source is a database.
35

What do COALESCE and NULLIF do in SQL?

SQLEasy
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COALESCE returns the first non-null value from its arguments, for example COALESCE(phone, alt_phone, 'Not given') fills missing phone numbers. NULLIF(a, b) returns NULL when a equals b, otherwise a. Its classic use is avoiding division by zero: revenue / NULLIF(orders, 0) returns NULL instead of an error when orders is zero. Together they handle most null-related reporting problems cleanly.
36

What is a factless fact table? Give an example.

Data Modelling & WarehousingHard
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A factless fact table records that an event or relationship happened but has no numeric measures, only dimension keys. Examples are student attendance (student, class, date) or which products were on promotion in which store each day. You analyse it by counting rows. A promotion coverage table is useful for questions like which promoted products did not sell, by comparing it with the sales fact.
37

What types of dashboard actions does Tableau offer?

TableauMedium
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Filter actions filter other sheets when a user selects or hovers on a mark, such as clicking a region to filter a product table. Highlight actions emphasise related marks without removing others. URL actions open a web page with field values in the link. Go to Sheet navigates to another dashboard. Parameter and set actions update a parameter or set based on selection, enabling advanced interactions like dynamic drill-downs and comparisons.
38

Write SQL to find the first-touch and last-touch channel for every user who converted.

Web & Marketing AnalyticsMedium
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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;
39

How would you find customers who never placed an order?

SQLMedium
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LEFT JOIN customers to orders and keep rows where order_id IS NULL, or use NOT EXISTS, which also handles NULLs safely.
40

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

SQLMedium
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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.

Frequently asked questions

What happens in the Data Analytics technical round round?

The technical round is the core of a Data Analytics interview: concept questions with follow-ups, a live hands-on task and a deep dive into one of your projects. Interviewers check depth, not just definitions.

How do I clear the technical round round?

Say your approach out loud before you start writing. After a correct answer, expect 'why?' and 'what if?' follow-ups; go one level deeper than the question. Pick one project you can explain end to end, including what you would do differently.

Which round comes next?

A typical order is online assessment, telephonic screening, one or two technical rounds, a managerial round and HR. Use the other round pages to practise each stage.

Practise every interview round

Online Assessment questionsTelephonic Interview questionsManagerial Round questionsRapid-Fire questionsHR & behavioural questionsFull mock interview

More ways to practise

Company-wise Data Analytics questionsPackage-wise Data Analytics questionsExperience-wise Data Analytics questionsRole-wise Data Analytics questionsCity-wise Data Analytics questionsMost asked Data Analytics questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsData Analytics mock interviewOnline assessment questionsTechnical round questionsManagerial round questionsData Analytics topic sheets