01
Explain the types of SQL joins.
Show answer
INNER returns matching rows, LEFT keeps all left rows, RIGHT keeps all right rows, FULL keeps all rows from both, and CROSS returns every combination.
EPAM Systems Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About EPAM Systems hiring for Data Analytics roles
EPAM Systems is a global digital platform engineering and software development services company. It works on product development, quality engineering, cloud and data projects for clients in finance, life sciences, retail and software. Its India delivery centres hire for software engineers, test automation engineers, data engineers and data scientists.
For Data Analytics roles, this EPAM Systems practice set focuses most on Tableau (6), Data Cleaning (5), Excel (5), Python for Analysis (4). 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
- Newtown, Pennsylvania, USA
- Main India offices
- Hyderabad, Bengaluru, Pune, Chennai, Gurugram
EPAM Systems Data Analytics interview process
- Online coding assessment
- Technical interview
- Technical or design round
- Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
What is the unit of randomisation in an experiment, and how do you choose it?
Show answer
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.
03
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
Show answer
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.
04
Write Python code to test whether conversion differs between control and treatment.
Show answer
from statsmodels.stats.proportion import proportions_ztest, proportion_confint
conversions = [1180, 1295] # control, treatment
visitors = [30000, 30100]
z, p = proportions_ztest(conversions, visitors)
print(f'z = {z:.2f}, p = {p:.4f}')
for c, n in zip(conversions, visitors):
low, high = proportion_confint(c, n, method='wilson')
print(f'rate {c/n:.3%} 95% CI {low:.3%} to {high:.3%}')
05
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.
06
What is the difference between COUNT(*), COUNT(column) and COUNT(DISTINCT column)?
Show answer
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.
07
Write SQL for a running total of sales by date.
Show answer
SELECT order_date,
SUM(amount) OVER (ORDER BY order_date) AS running_total
FROM sales;
08
How should you lay out a dashboard page?
Show answer
Readers scan from the top-left, so place the most important KPIs as cards across the top, then trend charts, then breakdowns and detail tables at the bottom. Group related visuals, align them on a grid and keep consistent spacing, fonts and colours. Put filters in one consistent place. Limit a page to roughly five to eight visuals that answer related questions, and give the page a clear title stating its purpose.
09
Write an Excel formula to look up a price using two criteria, product and city.
Show answer
=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
10
What is the difference between a list, tuple, dictionary and set in Python?
Show answer
A list is ordered and mutable, good for sequences you change, like [10, 20, 30]. A tuple is ordered but immutable, useful for fixed records such as coordinates. A dictionary stores key-value pairs with fast lookup by key, like {'Delhi': 120}. A set holds unique unordered values and is great for removing duplicates or checking membership quickly. Analysts use dictionaries constantly for mappings and renaming columns.
11
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").
12
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.
13
How can a chart mislead even when the numbers are correct?
Show answer
Common tricks are a truncated y-axis on bar charts, which exaggerates small differences; inconsistent scales across side-by-side charts; dual axes chosen to imply a correlation; cherry-picked time windows; cumulative charts that always rise; and area or 3D effects that distort size. Bars should start at zero, while line charts may zoom in if clearly labelled. Always check that the visual impression matches the actual magnitude.
14
Write pandas code to calculate customer acquisition cost and payback period in months for each channel.
Show answer
# spend: channel, spend
# customers: channel, customer_id, monthly_margin
new_cust = customers.groupby('channel').agg(
new_customers=('customer_id', 'nunique'),
avg_margin=('monthly_margin', 'mean'))
summary = spend.groupby('channel')['spend'].sum().to_frame().join(new_cust)
summary['cac'] = summary['spend'] / summary['new_customers']
summary['payback_months'] = summary['cac'] / summary['avg_margin']
print(summary.round(1).sort_values('payback_months'))
15
Write Python code to extract UTM source, medium and campaign from a column of landing page URLs.
Show answer
import pandas as pd
from urllib.parse import urlparse, parse_qs
def get_utms(url):
params = parse_qs(urlparse(str(url)).query)
return pd.Series({
'source': params.get('utm_source', ['(direct)'])[0].lower(),
'medium': params.get('utm_medium', ['(none)'])[0].lower(),
'campaign': params.get('utm_campaign', ['(not set)'])[0].lower(),
})
df[['source', 'medium', 'campaign']] = df['landing_url'].apply(get_utms)
16
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.
17
Write a CREATE TABLE statement for a sales fact table linked to date, product and store dimensions.
Show answer
CREATE TABLE fact_sales (
date_key INT NOT NULL REFERENCES dim_date(date_key),
product_key INT NOT NULL REFERENCES dim_product(product_key),
store_key INT NOT NULL REFERENCES dim_store(store_key),
invoice_no VARCHAR(20) NOT NULL, -- degenerate dimension
quantity INT NOT NULL,
net_amount DECIMAL(12,2) NOT NULL,
discount_amount DECIMAL(12,2) DEFAULT 0
);
18
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.
19
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.
20
Write pandas code to compute month-on-month percentage change and cumulative sales for each store.
Show answer
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
21
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.
22
How do you remove duplicates in Excel?
Show answer
Data tab, Remove Duplicates, pick the columns to compare. For a non-destructive way use UNIQUE() or conditional formatting to highlight repeats first.
23
What is the difference between users, sessions and events in Google Analytics 4?
Show answer
GA4 uses an event-based model: every interaction, such as page_view, scroll, add_to_cart or purchase, is an event with parameters. A session is a group of events from one user within a period of activity, ending after 30 minutes of inactivity by default. A user is identified by a device cookie, app instance or a logged-in user ID. One user can have many sessions, and each session many events.
24
What is a role-playing dimension?
Show answer
A role-playing dimension is one physical dimension used several times by the same fact table in different roles. The classic case is the date dimension: an orders fact has order date, ship date and delivery date keys, all pointing to the same date table. In SQL you join it three times with different aliases; in Power BI you keep one active relationship and use USERELATIONSHIP or create separate date views for each role.
25
How do you handle a dataset with 30% missing values in a key column?
Show answer
Understand why it is missing, consider imputation or a missing flag, test sensitivity, and talk to data owners about fixing collection if it is systematic.
26
What is Tableau and what are its main products?
Show answer
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.
27
Write a Tableau calculated field for profit ratio and a profit category.
Show answer
// Profit Ratio (aggregate first, then divide)
SUM([Profit]) / SUM([Sales])
// Profit Category
IF SUM([Profit]) < 0 THEN 'Loss'
ELSEIF [Profit Ratio] < 0.1 THEN 'Low margin'
ELSE 'Healthy'
END
// avoid AVG([Profit]/[Sales]): it averages row ratios and gives a wrong total
28
What are parameters in Tableau and how do you use them?
Show answer
A parameter is a single user-controlled value, such as a number, date or string, that is independent of the data. On its own it does nothing; you reference it in calculations, filters, reference lines or bins. Common uses are a Top N selector, a what-if growth rate, or a measure switcher where a CASE statement picks Sales, Profit or Quantity based on the parameter. Parameter actions let users set it by clicking a mark.
29
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.
30
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())
31
How do you detect and handle skewed data?
Show answer
Check histograms and skew(), then apply log, square-root or Box-Cox transforms, or use medians and robust statistics.
32
What is the peeking problem in A/B testing?
Show answer
Peeking means checking the p-value repeatedly during a test and stopping as soon as it drops below 0.05. Each look is another chance for random noise to look significant, so the real false positive rate can rise from 5% to 20% or more. Fix it by fixing the sample size in advance and analysing once, or by using methods built for continuous monitoring, such as sequential testing or always-valid confidence intervals.
33
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.
34
What is the difference between a calculated table in DAX and a table created in Power Query?
Show answer
A Power Query table is built during data refresh using M, before data enters the model, and is compressed like any imported table. A DAX calculated table is computed after load from other model tables, using functions such as CALENDAR, SUMMARIZE or DISTINCT. Prefer Power Query for shaping and cleaning source data. Use calculated tables for model-dependent needs, such as a date table from existing data or a small what-if table.
35
Tell me about a time you found a serious data quality issue.
Show answer
Situation: while building a returns report I noticed one warehouse had zero returns for three months. Task: confirm whether it was real. Action: I compared with the source system and found its return codes had changed after a software update, so my filter missed them. I informed the data owner, updated the mapping and back-filled the history. Result: the corrected report showed that warehouse had the highest return rate, which led to a packaging review.
36
How do you handle errors like #N/A and #DIV/0!?
Show answer
Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.
37
What is the difference between COUNT, COUNTA and COUNTIF?
Show answer
COUNT counts numbers, COUNTA counts non-empty cells and COUNTIF counts cells meeting one condition; COUNTIFS handles several conditions.
38
How do you handle null values in Tableau calculations?
Show answer
// treat missing sales as zero
ZN(SUM([Sales]))
// replace a missing value with a default
IFNULL([Region], 'Unknown')
// test explicitly
IF ISNULL([Discount]) THEN 0 ELSE [Discount] END
// right-click a null indicator on an axis to filter or show nulls at default position
39
What is the difference between dimensions and measures in Tableau?
Show answer
Dimensions are qualitative fields such as region, product or date that slice data into groups and create headers. Measures are quantitative fields such as sales or profit that get aggregated, by default with SUM. Dragging a dimension to rows creates one row per member; dragging a measure creates an axis. A numeric field like customer ID should be converted to a dimension so it is not summed.
40
Pin codes in your CSV lost their leading zeros after opening in Excel. Why and how do you prevent it?
Show answer
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.
Tips to crack the EPAM Systems Data Analytics interview
- Revise core concepts in Java, Python or JavaScript deeply, as EPAM interviews often test language fundamentals in detail.
- Prepare test automation framework design questions if applying for QA roles, including page object model and CI integration.
- Read about EPAM's engineering-first identity and its learning platforms, which it highlights for career growth.
- 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; EPAM Systems interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the EPAM Systems Data Analytics interview?
Candidates usually go through 4 stages: Online coding assessment; Technical interview; Technical or design round; Hiring manager 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 EPAM Systems Data Analytics interview?
This practice set leans on Tableau (6), Data Cleaning (5), Excel (5), Python for Analysis (4). Revise those first, then practise explaining one or two of your own projects end to end, because most EPAM Systems interviewers ask you to walk through real work.
Are these the exact questions asked at EPAM Systems?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like EPAM Systems's. They are not a leaked or verified list of past questions.
Where does EPAM Systems hire Data Analytics professionals in India?
EPAM Systems has large teams in Hyderabad, Bengaluru, Pune, Chennai, Gurugram. Openings and locations change often, so check the EPAM Systems careers page and filter by city and experience.
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