36 practice questions with answers for Data Analytics interviews in Lucknow.
Data Analytics jobs in Lucknow
Lucknow's tech job market is smaller but growing, led by IT-services delivery centres, government-tech projects and start-ups. Many Lucknow candidates target Noida and Gurgaon roles or remote jobs, so interviews follow the NCR pattern.
Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Lucknow.
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01
How do you let users choose Top N customers with a parameter in Tableau?
Create an integer parameter Top N with a range of 5 to 50 and show its control. Drag Customer to the Filters shelf, choose the Top tab, select By field, and set it to top Top N by SUM(Sales). Alternatively create a set based on the parameter so you can show Top N versus Others in one chart. If the Top N must respect a category filter, add that category filter to context.
02
What are variables (VAR) in DAX and why are they useful?
VAR stores the result of an expression inside a measure, and RETURN gives the final output. Variables make measures readable, avoid evaluating the same expression twice, and are evaluated in the filter context where they are defined, which helps avoid subtle bugs inside CALCULATE. They also make debugging easy, because you can temporarily RETURN a variable to check its value before returning the final result.
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
04
Write pandas code to calculate monthly revenue from an orders DataFrame.
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.
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.
Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.
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.
10
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.
11
How would you find customers who never placed an order?
Power is the probability that the test detects a real effect of a given size, commonly set at 80%. The minimum detectable effect is the smallest lift you care about and can reliably detect with your sample size. Smaller MDEs, lower baseline rates and higher power all need more users. If you can only get 10,000 users a week, you may only be able to detect large lifts, which should shape what you test.
13
Write Excel formulas to calculate ROAS and CPA for each campaign and flag campaigns below a ROAS target.
-- B = Spend, C = Conversions, D = Revenue, target ROAS in H1
E2 (ROAS): =IFERROR(D2/B2, 0)
F2 (CPA): =IF(C2=0, "No conversions", B2/C2)
G2 (Flag): =IF(E2<$H$1, "Below target", "OK")
-- Copy down, then add conditional formatting on G to highlight 'Below target'
14
What is the logical order in which SQL executes a SELECT query?
Although written as SELECT first, a query is logically processed as FROM and JOIN, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because SELECT runs later, but you can use it in ORDER BY. It also explains why aggregates belong in HAVING, not WHERE, and why window functions cannot be filtered in the same query level.
15
Write pandas code to find the top 2 products by revenue in each region.
-- 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"))
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.
Broadcasting lets NumPy perform operations on arrays of different shapes without copying data, by virtually stretching the smaller array. For example, subtracting a 1D array of column means from a 2D array centres every column in one line: X - X.mean(axis=0). Shapes are compatible when trailing dimensions are equal or one of them is 1. It is a key reason vectorised code is much faster than Python loops.
// 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
21
Write SQL to calculate daily active users and the DAU/MAU stickiness ratio.
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;
Common dimensions are accuracy, values reflect reality; completeness, required fields are present; consistency, the same fact matches across systems; validity, values follow formats and rules such as a 6-digit pin code; uniqueness, no unwanted duplicates; and timeliness, data is fresh enough for its use. Measuring these with simple checks, like percent missing or duplicate rate, turns vague complaints about bad data into fixable issues.
23
Find customers who ordered in January but not in February.
A context filter is applied before other dimension filters and before FIXED LOD calculations, creating a smaller dataset the rest of the view works on. Use it when a Top N filter should operate within a selected category, for example top 10 products within Electronics, or when a FIXED LOD must respect a filter. Add a filter to context from its right-click menu; it turns grey. Too many context filters can hurt performance.
25
Write a Python function that reads all Excel files in a folder and combines them into one DataFrame.
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.
27
Why are A/B tests tricky in two-sided marketplaces such as ride-hailing or food delivery?
In marketplaces, groups share supply, so the treatment can affect control. If treatment riders get a discount and book more, fewer drivers remain for control riders, which makes treatment look better than it would at full rollout. This violates the independence assumption. Teams use cluster randomisation by city or region, switchback tests that alternate treatment by time slots, or model the spillover when estimating impact.
28
Tell me about a time you used data to challenge an assumption.
Situation: our team believed weekend discounts drove most sales. Task: I was preparing the monthly review. Action: I compared discount and non-discount weekends over six months and controlled for paydays and festivals. Discounts moved sales forward from the following week but added little overall. I presented it with one clear chart. Result: management trialled fewer discount weekends, and margin improved without a fall in monthly revenue.
29
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.
Sampling bias occurs when the sample does not represent the population, so conclusions do not generalise. Common types: selection bias, such as surveying only app users about website issues; survivorship bias, studying only customers who stayed; non-response bias, when unhappy customers skip surveys; and convenience sampling, using whoever is easiest to reach. Random sampling, stratification and weighting help reduce it.
31
Write a DAX measure for year-over-year sales growth percentage.
Sales LY =
CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
Sales YoY % =
VAR cur = [Total Sales]
VAR prev = [Sales LY]
RETURN
DIVIDE(cur - prev, prev)
-- needs a marked date table; format the measure as a percentage
32
Write a Tableau calculation to find each customer's first purchase date and flag new versus repeat orders.
// 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])
33
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
Orphan records are child rows whose foreign key has no matching parent, such as orders with a customer_id that does not exist in the customer table. They break joins silently: an inner join drops them and totals fall short. Find them with a LEFT JOIN from the child to the parent and filter where the parent key IS NULL. Then fix the source, add an Unknown member, or report them separately.
34
Write Python code to fit a simple linear regression of sales on ad spend.
Sort by date (newest first), then Data → Remove Duplicates on the key column — Excel keeps the first occurrence. Or use UNIQUE / a COUNTIFS helper column.
36
What is the difference between sets and groups in Tableau?
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.
Which companies hire Data Analytics professionals in Lucknow?
Companies with large offices in Lucknow include HCLTech. Use their company pages below to practise employer-specific sets.
Are Data Analytics interviews in Lucknow different from other cities?
The questions depend more on the company than the city. Lucknow's mix of employers decides the style: Many Lucknow candidates target Noida and Gurgaon roles or remote jobs, so interviews follow the NCR pattern
How do I find Data Analytics jobs in Lucknow?
Shortlist companies with Lucknow offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Lucknow filter, and practise the questions on this page before each interview.