Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About PwC hiring for Data Analytics roles
PwC is one of the Big Four professional services networks, offering assurance, tax, consulting and deals services. In India, PwC member firms and acceleration centres support global clients with technology consulting, data analytics, cyber, risk and process work. It hires for associate and consultant roles in analytics, BI, testing, ERP and risk assurance.
For Data Analytics roles, this PwC practice set focuses most on Statistics and Case Studies (8), Power BI (6), Excel (5), Data Modelling & Warehousing (4). It has 3 easy, 31 medium, 6 hard questions, so you can start easy and work up to the harder ones.
Company type
Consulting
Headquarters
London, United Kingdom
Main India offices
Kolkata, Bengaluru, Mumbai, Hyderabad, Gurugram
PwC Data Analytics interview process
Online assessment (aptitude + technical)
Group discussion or case round
Technical interview
Partner or HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for PwCStart practicing
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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.
02
Sales dropped 20% last month. How will you analyse it?
Confirm data accuracy first → break down by region, product, channel and customer segment → compare with seasonality and last year → check pricing, stock-outs, marketing changes and funnel conversion → present the root causes with clear charts.
It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.
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.
07
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.
08
What is the normal distribution and the 68-95-99.7 rule?
The normal distribution is a symmetric, bell-shaped distribution described by its mean and standard deviation. The empirical rule says about 68% of values fall within one standard deviation of the mean, 95% within two and 99.7% within three. For example, if delivery times average 30 minutes with an SD of 5, about 95% arrive in 20 to 40 minutes. Many business metrics are skewed, so check before assuming normality.
09
How do you measure impact when a proper A/B test is not possible?
Use quasi-experimental methods. Difference-in-differences compares the change in a treated group, such as cities that got a new policy, with the change in similar untreated cities over the same period. Synthetic control builds a weighted mix of untreated regions to mimic the treated one. Regression discontinuity uses a cut-off, like a credit score threshold. Always check pre-period trends and state the assumptions clearly.
10
Estimate how many cups of tea are sold in a city of 1 crore people in a day.
State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
13
What are relationships and cardinality in Power BI?
Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.
14
Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?
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.
15
You share a forecasting workbook and users keep overwriting formulas. How do you protect it?
Separate input cells from calculation cells and colour inputs clearly. Unlock only the input cells through Format Cells, Protection, then protect the sheet so formulas cannot be edited, and optionally hide formulas. Add data validation on inputs to block wrong values such as negative quantities. Protect workbook structure so sheets cannot be deleted, keep a version history, and add a short instructions sheet so users know where to type.
Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.
18
Write SQL to calculate open rate and click-through rate for each email campaign.
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;
19
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
MERGE INTO dim_product t
USING stg_product s
ON t.product_id = s.product_id
WHEN MATCHED AND (t.name <> s.name OR t.category <> s.category) THEN
UPDATE SET name = s.name,
category = s.category,
updated_at = CURRENT_TIMESTAMP
WHEN NOT MATCHED THEN
INSERT (product_id, name, category, updated_at)
VALUES (s.product_id, s.name, s.category, CURRENT_TIMESTAMP);
22
What questions do you ask a stakeholder before building a dashboard?
Who will use it and what decisions will they make with it? Which three to five questions must it answer? What are the KPI definitions, targets and comparison periods? Which data sources are trusted? How often will they look at it, and on what device? Who needs access and are there security restrictions? What does success look like in a month? I summarise the answers in a short brief and get sign-off before building.
23
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.
24
Write a DAX measure for a KPI card showing achievement against target and a colour for conditional formatting.
Achievement % =
DIVIDE([Total Sales], [Sales Target])
KPI Colour =
VAR a = [Achievement %]
RETURN
SWITCH(
TRUE(),
ISBLANK(a), "#9E9E9E",
a >= 1, "#2E7D32",
a >= 0.9, "#F9A825",
"#C62828"
)
-- apply KPI Colour via Format > Callout value > fx > Field value
25
A junior analyst wants to load one huge flat table with every column into Power BI instead of a star schema. How do you advise them?
A flat table is quick to start and fine for a small one-off analysis. But at scale it bloats the model because repeated text attributes compress poorly, makes DAX filters and time intelligence harder, and breaks when you add a second fact like targets or returns. I would show them a star schema with one fact and a few dimensions, compare model size and measure speed, and let the results convince them.
Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.
LET assigns names to intermediate results inside a single formula, for example =LET(sales, SUM(D2:D100), cost, SUM(E2:E100), (sales-cost)/sales). It makes long formulas readable and faster, because a repeated sub-calculation is evaluated once instead of several times. It is especially useful with dynamic arrays, where the same FILTER result might otherwise be recalculated three or four times in one nested formula.
29
How do you calculate a simple customer lifetime value?
A simple formula is CLV = average order value x purchase frequency per year x gross margin x expected lifetime in years. Example: 800 rupees x 6 orders x 30 percent margin x 3 years = 4,320 rupees. Expected lifetime can be estimated as 1 divided by annual churn rate. A better version discounts future value and calculates CLV by cohort or segment, because averages hide very different customer groups.
The order database is the source of truth for counts and revenue. Analytics usually undercounts because of ad blockers, declined cookie consent, users closing the page before the confirmation tag fires, and payment redirects that break sessions. It can also overcount from duplicate tags on page refresh. Quantify the gap per month; if it is stable around 15%, use analytics for channel mix and trends and the database for totals.
32
You inherit a monthly Excel report and suspect some numbers are hard-typed over formulas. How do you audit it?
Use Go To Special and select Constants with Numbers to highlight hard-typed values inside formula areas. Show Formulas (Ctrl+`) to scan columns for inconsistencies, and use Trace Precedents and Dependents on key totals. Inquire or Spreadsheet Compare, if available, can flag inconsistent formulas. Then fix the overwritten cells, protect formula ranges and add check totals that compare with the source data each month.
33
What are additive, semi-additive and non-additive facts?
Additive facts, like sales amount or quantity, can be summed across every dimension. Semi-additive facts, like account balance or stock on hand, can be summed across accounts or stores but not across time; for time you take the closing or average value. Non-additive facts, like percentages, ratios or unit prices, cannot be summed at all, so you store their components (profit and revenue) and calculate the ratio at query time.
34
Tell me about a time you were at risk of missing a reporting deadline.
Situation: a month-end report was due at 10 am, and at 8 am the source data had not loaded. Task: deliver something reliable. Action: I informed my manager immediately, checked with the data team for an estimate, and prepared the report on the previous day's data, clearly labelled. Result: leadership got the report on time with a caveat, and I sent the final version by noon. We later added a data arrival alert.
35
What is CUPED and how does it speed up experiments?
CUPED is a variance reduction method that uses each user's pre-experiment behaviour, such as spend in the previous four weeks, as a covariate. You adjust the outcome by removing the part explained by past behaviour, which is unrelated to the treatment because assignment was random. The lower variance gives tighter confidence intervals, so you can detect the same effect with fewer users or a shorter test, often 20-50% faster.
36
The customer table has near-duplicates like 'Rahul Sharma, 98xxxx1234' and 'Rahul Sharma, +91 98xxxx1234'. How do you deduplicate?
First normalise: trim and lower-case names, collapse spaces, and strip country codes and symbols from phones and emails. Then match on strong keys such as phone or email. For remaining records use fuzzy matching on names with a similarity score, restricted to the same city or pin code to limit comparisons. Review borderline scores manually, keep a mapping from old IDs to a master ID, and fix entry at the source.
37
Tell me about a time you migrated or rebuilt reports in a new BI tool.
Situation: our team moved 15 Excel and older BI reports into Tableau. Task: I owned the sales set. Action: I listed every metric with its definition, rebuilt the shared data source first, then the dashboards, and ran side-by-side number checks for two weeks with the old reports. I held short demos for users. Result: all reports went live with matching numbers, and refresh effort fell from a day to an automatic schedule.
38
Average delivery time improved by 4 minutes, but customer complaints about late deliveries went up. How do you investigate?
An average can improve while the tail gets worse. I would look at the distribution: median, 90th and 95th percentiles and the share of orders beyond the promised time. Then break it down by city, time slot and restaurant to see where late orders cluster. Also check whether promised times changed, whether complaint tracking changed and whether a few very fast orders pulled the mean down.
39
How do you make a dashboard accessible, for example for colour-blind users?
Avoid red-green as the only difference; use colour-blind-safe palettes such as blue and orange, and add icons, labels or patterns so meaning never depends on colour alone. Ensure enough contrast between text and background, use readable font sizes, and add alt text to visuals. Keep a logical tab order for keyboard users, and offer a table view of chart data. Accessible dashboards are usually clearer for everyone.
A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis, usually no effect, were true. A p-value of 0.03 means that if the new page truly made no difference, results this strong would appear only about 3 percent of the time by chance. Below a chosen threshold such as 0.05 we reject the null. It is not the probability that the hypothesis is true.
Tips to crack the PwC Data Analytics interview
Learn about the PwC Professional framework, which covers whole leadership, business acumen, technical capabilities and relationships.
Practise case questions and guesstimates with a clear structure, especially for consulting and advisory roles.
Revise Excel, SQL and a BI tool, and be ready to describe how analysis helped a decision.
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; PwC interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the PwC Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); Group discussion or case round; Technical interview; Partner or 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 PwC Data Analytics interview?
This practice set leans on Statistics and Case Studies (8), Power BI (6), Excel (5), Data Modelling & Warehousing (4). Revise those first, then practise explaining one or two of your own projects end to end, because most PwC interviewers ask you to walk through real work.
Are these the exact questions asked at PwC?
No. These are curated practice questions that match the skills and difficulty usually tested in consulting interviews like PwC's. They are not a leaked or verified list of past questions.
Where does PwC hire Data Analytics professionals in India?
PwC has large teams in Kolkata, Bengaluru, Mumbai, Hyderabad, Gurugram. Openings and locations change often, so check the PwC careers page and filter by city and experience.