01
What does the CALCULATE function do?
Show answer
It evaluates an expression after modifying the filter context, for example CALCULATE(SUM(Sales[Amount]), Region[Name] = "North").
L&T Technology Services Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About L&T Technology Services hiring for Data Analytics roles
L&T Technology Services is an engineering research and development services company and part of the Larsen & Toubro group. It works with clients in transportation, industrial products, telecom, medical devices and plant engineering on product design, embedded systems and digital engineering. In India it hires engineers for software, embedded, testing and data roles across its engineering and delivery centres.
For Data Analytics roles, this L&T Technology Services practice set focuses most on SQL (8), Excel (6), Web & Marketing Analytics (5), Python for Analysis (3). It has 21 easy, 18 medium, 1 hard questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Vadodara, India
- Main India offices
- Vadodara, Bengaluru, Chennai, Mysuru, Pune
L&T Technology Services Data Analytics interview process
- Online assessment (aptitude + technical MCQs)
- Technical interview
- Managerial round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
What is the difference between a metric and a KPI?
Show answer
A metric is any quantity you measure, such as page views, tickets closed or average order value. A KPI, key performance indicator, is a metric chosen because it directly reflects progress towards an important business goal and usually has a target and owner. Every KPI is a metric, but most metrics are not KPIs. A good dashboard leads with a few KPIs and keeps supporting metrics for diagnosis.
03
Explain GROUP BY with an example.
Show answer
It groups rows sharing values so aggregates run per group, for example SELECT region, SUM(sales) FROM orders GROUP BY region.
04
SUMIFS vs COUNTIFS?
Show answer
SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions.
Example: =SUMIFS(Sales, Region, "North", Month, "Jan")
05
What is a pivot table and when do you use it?
Show answer
A pivot table summarises large data by dragging fields into rows, columns, values and filters — for example total sales by region and month — without writing formulas.
06
What is the difference between WHERE and HAVING?
Show answer
WHERE filters rows before grouping. HAVING filters groups after GROUP BY and can use aggregates like SUM() or COUNT().
07
What is data cleaning and why does it take so much of an analyst's time?
Show answer
Data cleaning means fixing or removing incorrect, incomplete, duplicated or inconsistent data so analysis is trustworthy. Real data comes from many systems and manual entry, so you meet typos, mixed formats, missing values and duplicates. Analysts often spend half or more of a project on it because every later chart and model depends on it. Wrong inputs give confident but wrong answers, which is worse than no answer.
08
What is the difference between loc and iloc in pandas?
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.
09
What is the difference between bounce rate and engagement rate in GA4?
Show answer
In GA4, an engaged session lasts longer than 10 seconds, has a conversion (key event) or has at least two page or screen views. Engagement rate is engaged sessions divided by total sessions. Bounce rate is simply the opposite: the share of sessions that were not engaged. This is more useful than the old single-page bounce definition, because a user who reads a full blog post for three minutes is no longer counted as a bounce.
10
What is regression to the mean? Give a business example.
Show answer
Regression to the mean means extreme results tend to be followed by more average results, simply because part of the extreme was luck. For example, if you pick the 10 worst-performing stores this quarter and give them a training programme, many will improve next quarter even without training. To claim the programme worked, compare them with similar low performers who did not get it.
11
Your NOT IN query unexpectedly returns zero rows. Why might that happen, and how do EXISTS and IN differ?
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.
12
What is the difference between a t-test and a z-test?
Show answer
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.
13
What is tidy data?
Show answer
Tidy data follows three rules: each variable is a column, each observation is a row, and each type of observational unit is a table. A sheet with months as columns is not tidy; unpivoting it to Region, Month and Sales columns makes it tidy. Tidy data is easy to filter, group, join and chart in pandas, SQL, Power BI and Tableau, so reshaping into this form is a core cleaning step.
14
What does the IF function do and how do you nest it?
Show answer
IF returns one result if a test is true and another if false. Nest IFs for multiple outcomes, or use IFS for cleaner formulas.
15
What is the difference between a view and a materialised view?
Show answer
A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.
16
What do CTR, CPC, CPA and ROAS mean in digital marketing?
Show answer
CTR, click-through rate, is clicks divided by impressions. CPC, cost per click, is spend divided by clicks. CPA, cost per acquisition, is spend divided by conversions such as orders or leads. ROAS, return on ad spend, is revenue from ads divided by ad spend, so a ROAS of 4 means 4 rupees of revenue per rupee spent. ROAS ignores product margin, so a high ROAS can still lose money.
17
What are UTM parameters and why do they matter?
Show answer
UTM parameters are tags added to a URL so analytics tools know where a visit came from. The main ones are utm_source (instagram, newsletter), utm_medium (paid_social, email, cpc), utm_campaign (diwali_sale_2026), plus optional utm_content and utm_term. Without them, traffic from emails, messaging apps and many social links shows up as direct or referral, and you cannot measure which campaign drove sign-ups or sales.
18
Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.
Show answer
df['date'] = pd.to_datetime(df['date'])
daily = (df.groupby('date')['sales'].sum()
.asfreq('D', fill_value=0)) # adds missing days as 0
weekly = daily.resample('W-MON', label='left', closed='left').sum()
print(weekly.head())
# W-MON with label/closed left gives weeks starting on Monday
19
Tell me about a time you worked with a marketing team to improve a campaign using data.
Show answer
Situation: a lead generation campaign had a high cost per lead and sales said leads were poor. Task: find what to change. Action: I joined ad data with CRM outcomes and found two audience segments produced most leads but almost no closed deals, while one smaller segment converted well. Result: we moved budget to that segment and changed the form questions; cost per qualified lead fell about 30% in six weeks.
20
How do you write a good hypothesis for an experiment?
Show answer
A good hypothesis names the change, the expected effect, the metric and the reason. For example: showing delivery date on the product page will increase add-to-cart rate by at least 2% because uncertainty about delivery stops purchases. It should be written before the test starts, be measurable, and state the null hypothesis of no difference. A vague hypothesis like 'the new page will be better' leads to cherry-picking metrics afterwards.
21
Why does correlation not imply causation? Give a business example.
Show answer
Two variables can move together because of a third factor, reverse causation or pure chance. Example: stores with more staff have higher sales, but busy locations get more staff because they already sell more, so hiring alone may not raise sales. Ice-cream sales and AC sales both rise in summer because of temperature. To claim causation you need experiments, natural experiments or careful control of confounders.
22
Write SQL to calculate users, conversions and conversion rate for each test variant.
Show answer
SELECT
a.variant,
COUNT(DISTINCT a.user_id) AS users,
COUNT(DISTINCT o.user_id) AS converters,
ROUND(100.0 * COUNT(DISTINCT o.user_id)
/ COUNT(DISTINCT a.user_id), 2) AS conversion_pct
FROM experiment_assignments a
LEFT JOIN orders o
ON o.user_id = a.user_id
AND o.order_ts >= a.assigned_ts
WHERE a.experiment_id = 'checkout_v2'
GROUP BY a.variant;
23
RANK vs DENSE_RANK vs ROW_NUMBER?
Show answer
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).
24
What is dimensional modelling?
Show answer
Dimensional modelling is a way of designing analytical tables around business processes. Numeric events such as sales or payments go into fact tables, and descriptive context such as customer, product, store and date goes into dimension tables. The design is easy for business users to understand and fast for BI tools to aggregate. For example, a retail model might have one sales fact joined to date, product, store and customer dimensions.
25
What is DAX and how does it differ from Excel formulas?
Show answer
DAX is the formula language for Power BI models. It works on columns and tables with filter context rather than on individual cells.
26
What are absolute and relative cell references?
Show answer
Relative references change when copied (A1); absolute references stay fixed ($A$1); mixed references lock only the row or column (A$1 or $A1).
27
What is a good structure for a data story?
Show answer
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 is the difference between relationships, joins and data blending in Tableau?
Show answer
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.
29
What is the difference between Goal Seek and Solver?
Show answer
Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
30
What is a slowly changing dimension? Explain Type 1 and Type 2.
Show answer
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.
31
Write a Python function that reads all Excel files in a folder and combines them into one DataFrame.
Show answer
from pathlib import Path
import pandas as pd
def combine_excel(folder):
frames = []
for f in Path(folder).glob('*.xlsx'):
df = pd.read_excel(f)
df['source_file'] = f.name
frames.append(df)
return pd.concat(frames, ignore_index=True)
all_sales = combine_excel('monthly_reports')
32
Write a Tableau calculation to find each customer's first purchase date and flag new versus repeat orders.
Show answer
// 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
How do you define and choose KPIs?
Show answer
Pick metrics tied to a business goal that are measurable, actionable and owned by someone, and pair a lagging metric with a leading one.
34
What is a primary key vs a foreign key?
Show answer
A primary key uniquely identifies each row in its table; a foreign key references another table's primary key to enforce relationships.
35
What is a snowflake schema and when would you choose it over a star schema?
Show answer
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.
36
Orders arrive in the warehouse with customer IDs that do not exist yet in the customer dimension. What do you do?
Show answer
These are late-arriving dimensions. Do not drop the orders, or revenue will be understated. Common practice is to insert a placeholder customer row with the natural key and 'Unknown' attributes, link the order to it, and update that row when full details arrive. Alternatively map to a single Unknown member with key -1. Also monitor how many such rows appear daily, because a spike usually means a broken feed.
37
Write SQL to calculate sessions, orders and conversion rate by traffic source.
Show answer
SELECT
s.traffic_source,
COUNT(*) AS sessions,
COUNT(o.order_id) AS orders,
ROUND(100.0 * COUNT(o.order_id) / COUNT(*), 2) AS conversion_pct
FROM sessions s
LEFT JOIN orders o
ON o.session_id = s.session_id
GROUP BY s.traffic_source
ORDER BY sessions DESC;
-- assumes at most one order per session; otherwise use COUNT(DISTINCT o.session_id)
38
What is the difference between a table and a normal range?
Show answer
A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.
39
What is a correlated subquery?
Show answer
A correlated subquery refers to a column from the outer query, so it is logically evaluated once per outer row. Example: find employees earning above their department average with WHERE salary > (SELECT AVG(salary) FROM emp e2 WHERE e2.dept_id = e1.dept_id). It is readable but can be slow on large tables. The same result is often faster with a window function such as AVG(salary) OVER (PARTITION BY dept_id).
40
What is a holdout group and why do teams keep one?
Show answer
A holdout group is a small share of users, often 5-10%, kept out of a feature or of all marketing campaigns for a long time. Comparing them with everyone else shows the cumulative long-term impact, which individual short tests miss, and checks whether many small wins really add up. For example, a notification holdout shows whether push messages raise monthly retention or just shift activity earlier.
Tips to crack the L&T Technology Services Data Analytics interview
- Revise core engineering subjects from your branch along with C, basic data structures and one scripting language before the technical round.
- Be ready to explain an academic or internship project end to end, including design choices, tools used and testing approach.
- Read about LTTS segments like mobility, medical devices and plant engineering so you can connect your skills to a domain.
- 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; L&T Technology Services interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the L&T Technology Services Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical MCQs); 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 L&T Technology Services Data Analytics interview?
This practice set leans on SQL (8), Excel (6), Web & Marketing Analytics (5), Python for Analysis (3). Revise those first, then practise explaining one or two of your own projects end to end, because most L&T Technology Services interviewers ask you to walk through real work.
Are these the exact questions asked at L&T Technology Services?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like L&T Technology Services's. They are not a leaked or verified list of past questions.
Where does L&T Technology Services hire Data Analytics professionals in India?
L&T Technology Services has large teams in Vadodara, Bengaluru, Chennai, Mysuru, Pune. Openings and locations change often, so check the L&T Technology Services careers page and filter by city and experience.
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