Birlasoft Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Birlasoft hiring for Data Analytics roles
Birlasoft is an Indian IT services company and part of the CK Birla Group. It focuses on enterprise solutions such as Oracle, SAP and Microsoft applications, along with cloud, data and testing services for manufacturing, life sciences and energy clients. In India it hires for software engineers, ERP consultants, test engineers and data analysts.
For Data Analytics roles, this Birlasoft practice set focuses most on Excel (8), Statistics and Case Studies (6), Python for Analysis (5), A/B Testing & Experiments (4). It has 19 easy, 20 medium, 1 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Pune, India
Main India offices
Pune, Noida, Bengaluru, Chennai
Birlasoft Data Analytics interview process
Online assessment (aptitude + reasoning)
Technical MCQs
Technical interview
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
What is the Central Limit Theorem and why does it matter for analysts?
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, usually from about 30 observations, even if the underlying data is skewed. This is why we can use z-tests, t-tests and confidence intervals for averages such as order value or session time. It does not make the raw data normal, and very heavy-tailed data may need larger samples.
You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.
03
Your A/B test ends with no significant difference. How do you report it?
A flat result is still useful. Report the observed difference with its confidence interval, for example -0.3% to +0.8%, which shows the range of effects still possible. State whether the test had enough power for the planned MDE. If it did, conclude that any effect is probably small and the team can choose based on cost or simplicity. Share learnings so the same idea is not retested blindly.
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.
05
Marketing wants to A/B test two email subject lines on a list of 2,000 subscribers. What do you advise?
With 1,000 per group and a typical click rate of 3%, only a very large difference, roughly 70% relative or more, would be detectable, so a small real lift would look like noise. I would suggest testing on a larger send, pooling several campaigns, or using open-to-click rate with a bigger effect. If they still run it, report the confidence interval and treat the result as directional.
A lambda is a small anonymous function written in one line, for example lambda x: x * 1.18 to add GST. Analysts use lambdas with sorted for custom sort keys, with pandas assign for computed columns, for example assign(margin=lambda d: d.profit / d.sales), and occasionally with apply. For anything longer than one expression or reused in several places, a normal def function is clearer and easier to test.
08
Checkout conversion fell from 3.2% to 2.6% the week after an app release. How do you investigate?
First confirm it is real: check tracking events, data delays and whether the denominator changed. Then segment by app version, platform, device, payment method and traffic source. If the drop is concentrated in the new version or one payment option, it points to a bug or UX change. Look at funnel steps to find where users drop. Compare with seasonality and campaigns, then share findings with product and engineering quickly.
09
What is the difference between bounce rate and engagement rate in GA4?
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
A stakeholder says the Power BI report shows different revenue than their Excel file. How do you troubleshoot?
I would first agree on definitions: gross or net, including tax or returns, order date or invoice date. Then compare one small slice, such as one month and one region, in both. I check filters on the report and page, relationships that drop unmatched rows, refresh time versus the Excel extract time, and duplicate rows. Once the gap is explained, I document the definition in the report so the question does not repeat.
11
What is the difference between Goal Seek and Solver?
Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
Open the semantic model's refresh history in the Service to see whether refreshes failed, for example due to expired credentials, an offline gateway or a timeout. Check whether the report is connected to a different copy of the model, whether the source itself was updated, and whether incremental refresh only covers some partitions. Also confirm the schedule time zone. Finally, add a 'data as of' timestamp on the report.
14
What are pre-attentive attributes and how do you use them in charts?
Pre-attentive attributes are visual properties the brain notices in a fraction of a second before conscious thought, such as colour, size, position, length and bold weight. Use them to direct attention: make all bars grey and highlight only the declining region in a strong colour, or bold the one number that matters. Using too many highlights cancels the effect, so emphasise one or two things per chart.
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.
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.
20
Tell me about a time an experiment result went against what the team expected.
Situation: the team was sure a shorter signup form would raise registrations. Task: I owned the test analysis. Action: registrations rose 6%, but I checked the guardrail and found seven-day activation fell, because we had removed a question that personalised onboarding. I shared both results with the segment breakdown. Result: we kept the short form but moved the question to the first screen after signup, which recovered activation.
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.
22
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.
23
Write Excel formulas to get a sorted list of unique customer names and their count.
-- unique customers from column B, sorted A to Z
=SORT(UNIQUE(B2:B1000))
-- number of unique customers
=COUNTA(UNIQUE(B2:B1000))
-- unique customers sorted by total sales, highest first
=SORTBY(UNIQUE(B2:B1000), SUMIFS(D2:D1000, B2:B1000, UNIQUE(B2:B1000)), -1)
24
How do you remove duplicates in Excel but keep the latest record?
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.
25
What does value_counts do in pandas, and how do you show percentages instead of counts?
value_counts returns how many times each unique value appears in a Series, sorted from most to least frequent. It is a quick way to check category distribution or spot typos. Pass normalize=True to get proportions, then multiply by 100 for percentages, for example df['city'].value_counts(normalize=True).mul(100).round(1). Use dropna=False to include missing values, which are excluded by default and are often important.
A subquery is inline; a CTE is a named, readable block for one statement; a temp table is physically stored for the session and can be indexed and reused.
28
Brand keyword ads show a ROAS of 15, and the team wants to double the budget. How do you respond?
Brand search ROAS is usually inflated because people searching the brand name were already looking for you and would often click the free organic result anyway. Before increasing spend, I would propose an incrementality test, such as pausing brand ads in some matched cities or time periods and measuring the drop in total brand-search orders. The incremental ROAS is often far lower, so the extra budget may work better elsewhere.
29
A report filters WHERE order_date = '2026-03-31' and misses almost all orders from that day. Why?
order_date is probably a DATETIME or TIMESTAMP column, so the filter matches only rows at exactly midnight, 2026-03-31 00:00:00. Use a half-open range instead: WHERE order_date >= '2026-03-31' AND order_date < '2026-04-01'. This keeps the index usable, unlike wrapping the column in CAST or DATE(), and it correctly includes orders at 23:59:59.500. Also check the time zone of the stored timestamps.
30
What is the difference between dimensions and measures in Tableau?
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.
31
What is the difference between a t-test and a z-test?
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.
COALESCE returns the first non-null value from its arguments, for example COALESCE(phone, alt_phone, 'Not given') fills missing phone numbers. NULLIF(a, b) returns NULL when a equals b, otherwise a. Its classic use is avoiding division by zero: revenue / NULLIF(orders, 0) returns NULL instead of an error when orders is zero. Together they handle most null-related reporting problems cleanly.
33
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.
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.
35
What is the unit of randomisation in an experiment, and how do you choose it?
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.
36
What is the difference between a calculated table in DAX and a table created in Power Query?
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.
37
What is the difference between concat, merge and join in pandas?
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.
38
What is a date table in Power BI and why should you mark it as one?
A date table has one row per day with no gaps, covering the full range of your data, plus columns like year, month, quarter and week. Marking it as a date table tells Power BI to use it for time intelligence functions such as TOTALYTD and SAMEPERIODLASTYEAR. Without it, those functions can give wrong results and auto date tables bloat the model. Relate it to every fact table's date column.
Data → Data Validation restricts what can be entered in a cell (lists, number ranges, dates), reducing manual entry errors in shared sheets.
Tips to crack the Birlasoft Data Analytics interview
Learn basic ERP concepts such as procure-to-pay and order-to-cash, as many Birlasoft projects involve enterprise applications.
Revise SQL and data basics, since ERP and analytics roles both rely on strong querying skills.
Read about the CK Birla Group and Birlasoft's manufacturing and life sciences client focus.
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; Birlasoft interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Birlasoft Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Technical MCQs; 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 Birlasoft Data Analytics interview?
This practice set leans on Excel (8), Statistics and Case Studies (6), Python for Analysis (5), A/B Testing & Experiments (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Birlasoft interviewers ask you to walk through real work.
Are these the exact questions asked at Birlasoft?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Birlasoft's. They are not a leaked or verified list of past questions.
Where does Birlasoft hire Data Analytics professionals in India?
Birlasoft has large teams in Pune, Noida, Bengaluru, Chennai. Openings and locations change often, so check the Birlasoft careers page and filter by city and experience.