01
How do you decide the sample size for a survey?
Show answer
It depends on margin of error, confidence level and expected variability; larger samples give smaller errors with diminishing returns.
Zensar Technologies Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Zensar Technologies hiring for Data Analytics roles
Zensar Technologies is an Indian IT services company and part of the RPG Group. It provides digital experience, application engineering, testing, cloud and data services to clients in manufacturing, retail, financial services and hi-tech. In India it hires for software engineers, test engineers and data professionals.
For Data Analytics roles, this Zensar Technologies practice set focuses most on SQL (9), Data Modelling & Warehousing (5), Statistics and Case Studies (4), Power BI (4). It has 16 easy, 24 medium questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Pune, India
- Main India offices
- Pune, Hyderabad, Bengaluru
Zensar Technologies Data Analytics interview process
- Online assessment (aptitude + reasoning)
- Coding test
- Technical interview
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
How do you read a CSV and inspect it in Pandas?
Show answer
df = pd.read_csv('file.csv'); then head(), info(), describe() and isna().sum() for a quick health check.
03
What is Row-Level Security?
Show answer
Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.
04
Which KPIs would you track for an e-commerce app?
Show answer
DAU/MAU, conversion rate, average order value, cart abandonment, retention/churn, CAC, LTV, return rate and NPS.
05
Explain the common marketing attribution models.
Show answer
Attribution decides how credit for a conversion is shared across touchpoints. Last click gives all credit to the final touch, first click to the first one. Linear splits credit equally, time decay gives more to recent touches, and position-based gives most to first and last. Data-driven attribution uses an algorithm on your own paths to estimate each channel's contribution. Each model answers a different question, so state which one a report uses.
06
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.
07
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.
08
How do you calculate a running total?
Show answer
SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.
09
What is an A/B test and why is random assignment so important?
Show answer
An A/B test is a controlled experiment where users are split into a control group that sees the current version and a treatment group that sees a change, and you compare a metric such as conversion. Random assignment makes the groups similar in every way except the change, including things you cannot measure. That is why a difference in results can be attributed to the change itself, not to user mix or seasonality.
10
What is the difference between a fact table and a dimension table?
Show answer
A fact table stores measurable events at a defined grain, such as one row per order line, with numeric measures like quantity and amount plus foreign keys. Dimension tables describe the context of those events: customer, product, store and date, with descriptive attributes used for filtering and grouping. Facts are long and grow quickly; dimensions are wider and smaller. Together they form star or snowflake schemas.
11
Write SQL to find the number of days between each customer's consecutive orders and their average gap.
Show answer
WITH gaps AS (
SELECT customer_id,
order_date,
order_date - LAG(order_date) OVER (
PARTITION BY customer_id ORDER BY order_date) AS days_since_prev
FROM orders
)
SELECT customer_id,
COUNT(*) AS orders,
AVG(days_since_prev) AS avg_gap_days
FROM gaps
GROUP BY customer_id
HAVING COUNT(*) > 1;
-- order_date is DATE (PostgreSQL); use DATEDIFF in other databases
12
What is statistical significance vs practical significance?
Show answer
A result can be statistically significant yet too small to matter; always report effect size and business impact together with the p-value.
13
What is a gateway in Power BI?
Show answer
A bridge that lets the Service refresh data from on-premises sources securely on a schedule.
14
How do you let users choose Top N customers with a parameter in Tableau?
Show answer
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.
15
How do you choose the right chart?
Show answer
Line for trends, bar for comparisons, scatter for relationships, histogram for distributions and pie only for few parts of a whole.
16
What is a conformed dimension?
Show answer
A conformed dimension is one dimension table with the same keys, attributes and meanings shared by several fact tables. For example, one date and one product dimension used by sales, returns and inventory facts. Because they conform, you can compare sales and returns by the same product category in one report without mismatched definitions. Conformed dimensions are the main way an enterprise warehouse stays consistent across departments.
17
How are NULLs handled in SQL?
Show answer
NULL means unknown, so comparisons with = fail; use IS NULL, COALESCE for defaults and remember most aggregates ignore NULLs except COUNT(*).
18
What are table calculations in Tableau?
Show answer
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.
19
Write pandas code to extract month name, weekday and hour from a timestamp column.
Show answer
df['ts'] = pd.to_datetime(df['ts'], errors='coerce')
df['month'] = df['ts'].dt.month_name()
df['weekday'] = df['ts'].dt.day_name()
df['hour'] = df['ts'].dt.hour
df['is_weekend'] = df['ts'].dt.dayofweek >= 5
# orders by weekday
print(df['weekday'].value_counts())
20
What is a degenerate dimension?
Show answer
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.
21
What is a window function?
Show answer
A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).
22
What do the match_mode and search_mode arguments of XLOOKUP control?
Show answer
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.
23
What is the difference between Power BI Desktop, Power BI Service and Power BI Report Server?
Show answer
Power BI Desktop is the free Windows application where you connect data, model it, write DAX and design reports. Power BI Service is the cloud platform where reports are published, shared, scheduled for refresh, and organised into workspaces and apps. Power BI Report Server is an on-premises option for organisations that cannot use the cloud, with fewer features. Most analysts build in Desktop and share through the Service.
24
Write a Tableau table calculation for year-over-year sales growth.
Show answer
// 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
25
Tell me about a time you had to say no to a stakeholder's request.
Show answer
Situation: a regional manager asked me to show his team's numbers excluding two weeks of a stock-out. Task: be helpful without misreporting. Action: I explained that removing the period would make his region incomparable with others, and offered an annotation and a separate view showing the stock-out impact. Result: he accepted, the leadership review understood the dip, and the main dashboard stayed consistent for every region.
26
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.
27
Write SQL to calculate open rate and click-through rate for each email campaign.
Show answer
SELECT
s.campaign_id,
COUNT(DISTINCT s.user_id) AS delivered,
COUNT(DISTINCT o.user_id) AS opened,
COUNT(DISTINCT c.user_id) AS clicked,
ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT s.user_id), 1) AS open_rate,
ROUND(100.0 * COUNT(DISTINCT c.user_id) / COUNT(DISTINCT s.user_id), 1) AS ctr
FROM email_sends s
LEFT JOIN email_opens o ON o.campaign_id = s.campaign_id AND o.user_id = s.user_id
LEFT JOIN email_clicks c ON c.campaign_id = s.campaign_id AND c.user_id = s.user_id
WHERE s.status = 'delivered'
GROUP BY s.campaign_id;
28
What is a z-score and how is it used?
Show answer
A z-score tells how many standard deviations a value is from the mean: z = (x minus mean) divided by standard deviation. A delivery time with z = 2.5 is unusually slow. Analysts use z-scores to flag outliers, often beyond plus or minus 3, to compare values on different scales, such as marks in different exams, and to look up probabilities when data is roughly normal.
29
How do you calculate working days between two dates in Excel, excluding holidays?
Show answer
-- A2 = start date, B2 = end date, Holidays = named range of holiday dates
=NETWORKDAYS(A2, B2, Holidays)
-- if the weekend is only Sunday (weekend code 11)
=NETWORKDAYS.INTL(A2, B2, 11, Holidays)
-- date that is 10 working days after A2
=WORKDAY(A2, 10, Holidays)
30
What is the difference between a surrogate key and a natural key?
Show answer
A natural key comes from the source system and has business meaning, such as a customer mobile number or product SKU. A surrogate key is a meaningless integer or hash generated in the warehouse. Surrogate keys protect the model when source keys change, get reused or clash across systems, and they are essential for slowly changing dimensions where one customer has several historical rows. Keep the natural key as an attribute for lookups.
31
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).
32
What does CASE WHEN do?
Show answer
It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.
33
What is cohort analysis?
Show answer
Grouping users by a shared start date or trait and tracking behaviour over time, for example monthly retention per signup month.
34
What is a data mart?
Show answer
A data mart is a subset of the warehouse built for one department or subject, such as sales, finance or HR. It contains the facts and dimensions that team needs, often pre-aggregated and with business-friendly names. Marts make reporting faster and simpler for that team. Ideally they are built from the central warehouse using conformed dimensions, so the finance mart and the sales mart still agree on revenue.
35
Write SQL using LAG to compare each month's revenue with the previous month.
Show answer
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month,
SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 1) AS mom_pct
FROM monthly
ORDER BY month;
36
What is the difference between concat, merge and join in pandas?
Show answer
concat stacks DataFrames vertically (more rows) or side by side along an axis, for example appending monthly files with the same columns. merge combines DataFrames on key columns, like a SQL join, with how='inner', 'left', 'right' or 'outer'. join is a shortcut for merging on the index. Use concat to append and merge to look up related data. Always check row counts after a merge.
37
When would you use a one-tailed test instead of a two-tailed test?
Show answer
A two-tailed test checks for a difference in either direction and is the safer default for A/B tests. A one-tailed test checks only one direction, such as treatment being better, and needs fewer users. Use it only if you decided beforehand that you would act the same way whether the change has no effect or a negative effect. Otherwise you may miss harm, which is why most teams stay two-tailed.
38
What is a PivotTable and when do you use it?
Show answer
A tool that summarises large tables by dragging fields into rows, columns, values and filters, so you can slice sales by region or month without writing formulas.
39
What is Power Query and why is it useful?
Show answer
An ETL tool inside Excel for importing, cleaning and shaping data with recorded steps that refresh automatically when the source changes.
40
Write SQL to find the median order value overall and per city.
Show answer
-- Works in PostgreSQL, Snowflake, SQL Server (with OVER for SQL Server)
SELECT
city,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY order_value) AS median_value,
AVG(order_value) AS mean_value
FROM orders
GROUP BY city
ORDER BY median_value DESC;
Tips to crack the Zensar Technologies Data Analytics interview
- Practise aptitude and basic programming questions for the online test, along with simple SQL queries.
- Read about the RPG Group and Zensar's focus on digital experience and engineering services.
- Prepare to explain your projects clearly, including tools used and testing done, in a short structured answer.
- 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; Zensar Technologies interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Zensar Technologies Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Coding test; Technical interview; 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 Zensar Technologies Data Analytics interview?
This practice set leans on SQL (9), Data Modelling & Warehousing (5), Statistics and Case Studies (4), Power BI (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Zensar Technologies interviewers ask you to walk through real work.
Are these the exact questions asked at Zensar Technologies?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Zensar Technologies's. They are not a leaked or verified list of past questions.
Where does Zensar Technologies hire Data Analytics professionals in India?
Zensar Technologies has large teams in Pune, Hyderabad, Bengaluru. Openings and locations change often, so check the Zensar Technologies careers page and filter by city and experience.
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