36 practice questions with answers for Data Analytics interviews in Delhi.
Data Analytics jobs in Delhi
Delhi NCR has one of India's biggest pools of IT, consulting, banking and start-up jobs. Delhi itself hosts many corporate, government-tech and consulting offices, and most candidates also apply across Noida and Gurgaon, so interviews here mix service-company and product-company styles.
Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Delhi.
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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.
02
Write pandas code for a pivot table of sales by region and category with sum and count.
A slowly changing dimension handles attributes that change over time, like a customer's city. Type 1 overwrites the old value, so history is lost and past sales appear under the new city. Type 2 adds a new row with a new surrogate key plus valid_from, valid_to and is_current columns, so old sales stay linked to the old city. Type 2 preserves accurate history but makes the table larger and joins more careful.
Situation: my manager wanted to report average resolution time for support tickets. Task: I felt a few very old tickets distorted the average. Action: I prepared a quick comparison showing mean versus median and the percentage resolved within 24 hours, and explained why the median was more representative. Result: we agreed to show median and the 24-hour rate, keeping the mean in a tooltip, and the team stopped chasing misleading spikes.
08
What do the match_mode and search_mode arguments of XLOOKUP control?
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.
09
A junior analyst wants to load one huge flat table with every column into Power BI instead of a star schema. How do you advise them?
A flat table is quick to start and fine for a small one-off analysis. But at scale it bloats the model because repeated text attributes compress poorly, makes DAX filters and time intelligence harder, and breaks when you add a second fact like targets or returns. I would show them a star schema with one fact and a few dimensions, compare model size and measure speed, and let the results convince them.
10
Write pandas code to extract month name, weekday and hour from a timestamp column.
Situation: a sales head kept adding new metrics while I was building a Power BI report. Task: deliver on time without endless rework. Action: I wrote a one-page scope with the agreed KPIs, shared a rough mock-up first, and logged new asks in a backlog with effort estimates. We agreed phase one and phase two. Result: phase one went live on schedule, and later requests were planned instead of disrupting the work.
The Data Model is an in-memory columnar store inside Excel that holds several related tables, and Power Pivot is the interface to manage it. Instead of flattening data with VLOOKUPs, you load fact and dimension tables, create relationships and write DAX measures. It handles millions of rows that would not fit on a sheet, and PivotTables built on it can slice across tables. It is the same engine idea that Power BI uses.
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.
15
A stakeholder asks for a pie chart of 12 product categories. When is a pie chart acceptable, and what would you suggest?
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.
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).
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
18
What is the difference between relationships, joins and data blending in Tableau?
Relationships, the default logical layer, connect tables flexibly and Tableau decides join type and level per view, avoiding duplicated measures. Joins physically merge tables into one at a fixed level, which can duplicate rows if grains differ. Blending combines data from separate data sources at the view level using a primary and secondary source, aggregating the secondary first. Prefer relationships, use joins for specific row-level needs, and blending as a last resort.
19
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
20
What is the difference between discrete and continuous fields in Tableau?
Discrete fields, shown as blue pills, have distinct values and create headers, for example month names as separate columns. Continuous fields, shown as green pills, form an unbroken range and create an axis, for example a continuous date line chart. The same date can be either: discrete months give a bar per month label, continuous months give a time-series axis. Colour also changes: discrete gives separate colours, continuous a gradient.
21
Your sales data contains negative quantities. What do you do with them?
Do not delete them blindly. First find out what they mean: often they are returns, cancellations or credit notes, which are valid business events. Check with the source team or a documentation note. If they are returns, keep them but report gross sales, returns and net sales separately. If some are clearly entry errors, such as a negative quantity on a new order, flag and correct them and log the rule.
22
What is the difference between a calculated field and a helper column in Excel?
A calculated field lives inside a PivotTable and works on summarised data; a helper column is a normal column of formulas that feeds the PivotTable or other formulas.
23
What KPIs would you track for a customer support call centre?
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.
24
Your manager wants a visual that always shows the top 5 products for whatever region is selected in a slicer. How do you build it?
The simplest way is a visual-level Top N filter on Product, by the Total Sales measure, set to 5; it recalculates for every slicer selection. If the number must be user selectable, create a numeric what-if parameter and a ranking measure using RANKX over ALLSELECTED(Product), then filter the visual where rank is less than or equal to the parameter. I would also add an Others row if the business wants totals to still match.
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.
26
Write Python code to fit a simple linear regression of sales on ad spend.
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.
28
What are additive, semi-additive and non-additive facts?
Additive facts, like sales amount or quantity, can be summed across every dimension. Semi-additive facts, like account balance or stock on hand, can be summed across accounts or stores but not across time; for time you take the closing or average value. Non-additive facts, like percentages, ratios or unit prices, cannot be summed at all, so you store their components (profit and revenue) and calculate the ratio at query time.
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.
https://www.example.com/sale?utm_source=instagram&utm_medium=paid_social&utm_campaign=summer_sale_2026&utm_content=reel_video_a
-- Rule 1: always lower case with underscores; 'Instagram' and 'instagram' become separate rows
-- Rule 2: keep a shared UTM sheet with allowed source and medium values so every team tags the same way
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.
33
How do you implement row-level security in Tableau?
The scalable approach is an entitlement table mapping usernames to allowed values, such as region managers to their regions. Relate or join it to the data, then add a calculated filter like USERNAME() = [Username] and apply it as a data source filter on the published source so it cannot be removed. For small cases, user filters or group-based functions like ISMEMBEROF work. Test by viewing as different users before release.
34
Write a Tableau table calculation for year-over-year sales growth.
// with YEAR(Order Date) in the view, Compute Using: Table (across)
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1))
/ ABS(LOOKUP(ZN(SUM([Sales])), -1))
// the built-in Quick Table Calculation 'Year over Year Growth' generates similar logic
// format the field as a percentage
35
Your pandas script shows SettingWithCopyWarning and a new column does not update. What is happening and how do you fix it?
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.
A confidence interval gives a range of plausible values for a population parameter based on a sample. A 95 percent interval for average order value of 820 to 880 rupees means that if we repeated the sampling many times, about 95 percent of such intervals would contain the true average. Wider intervals mean more uncertainty; larger samples narrow them. Reporting the interval is more honest than reporting one number.
Which companies hire Data Analytics professionals in Delhi?
Companies with large offices in Delhi include Samsung, Boston Consulting Group. Use their company pages below to practise employer-specific sets.
Are Data Analytics interviews in Delhi different from other cities?
The questions depend more on the company than the city. Delhi's mix of employers decides the style: Delhi itself hosts many corporate, government-tech and consulting offices, and most candidates also apply across Noida and Gurgaon, so interviews here mix service-company and product-company styles
How do I find Data Analytics jobs in Delhi?
Shortlist companies with Delhi offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Delhi filter, and practise the questions on this page before each interview.