Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About EY hiring for Data Analytics roles
EY is one of the Big Four professional services networks, offering assurance, tax, strategy and transactions, and consulting services. In India, EY GDS and EY member firms support global clients with technology consulting, data analytics, cybersecurity and risk work. It hires for analyst and consultant roles in data analytics, BI, testing, ERP and risk.
For Data Analytics roles, this EY practice set focuses most on Statistics and Case Studies (8), Power BI (8), Data Cleaning (5), Excel (4). It has 4 easy, 29 medium, 7 hard questions, so you can start easy and work up to the harder ones.
Company type
Consulting
Headquarters
London, United Kingdom
Main India offices
Bengaluru, Kolkata, Gurugram, Hyderabad, Kochi
EY Data Analytics interview process
Online assessment (aptitude + technical)
Technical interview
Managerial or case round
Partner or HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for EYStart practicing
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01
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 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.
MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.
06
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.
07
How do you handle large CSV files that do not fit in memory in Python?
A pie chart works only for showing parts of a whole with very few categories, two to four, where one share clearly dominates, such as 70 percent of orders are prepaid. Avoid it when there are many slices, when values are close, or when comparing across periods, because people judge angles poorly. A sorted bar chart is almost always easier to read and compare. Never use 3D pies.
A bridge table resolves a many-to-many relationship between a fact and a dimension. For example, one bank account can have several holders and one customer can hold several accounts, so a bridge table stores account_key, customer_key and often an allocation weight. Without it, joining facts to customers duplicates amounts. Use weights to split balances, or report clearly that totals across customers will exceed the true total.
12
A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?
Totals in DAX are not the sum of visible rows; the measure is recalculated in the total's filter context. A measure like a distinct count, a ratio or an IF condition can give a different result at total level. If the business wants the sum of row values, iterate explicitly, for example SUMX ( VALUES ( Product[Name] ), [My Measure] ). Confirm with stakeholders which total is correct before changing it.
13
How would you design an A/B test for a new checkout button?
Define one primary metric, such as checkout conversion, plus guardrails like refund rate. State the hypothesis and minimum detectable effect, then calculate sample size and duration for chosen power and significance. Randomise users, not sessions, into control and variant, check the split is balanced, and run for full weeks to cover weekday patterns. Do not peek and stop early. Finally analyse with a proportion test and check key segments.
Normally filters flow from the one side to the many side, for example from Product to Sales. Bidirectional filtering lets filters also flow back, so a Sales filter can limit Product. It helps in some many-to-many or bridge-table cases. The risks are ambiguous filter paths, unexpected results and slower performance. Prefer single direction and use CROSSFILTER inside a specific measure when you need both directions only there.
// Calculated field: Last 30 Days Flag
DATEDIFF('day', [Order Date], TODAY()) <= 29
AND [Order Date] <= TODAY()
// Put this field on Filters and keep True.
// For a sales measure instead of a filter:
// IF DATEDIFF('day', [Order Date], TODAY()) <= 29 THEN [Sales] END
18
Tell me about a time you handled a request for access to sensitive data.
Situation: a regional manager asked for a salary-level export of all employees to plan hiring. Task: help without breaking data policy. Action: I clarified the actual need, which was headcount cost by grade and location, checked with HR on access rules, and built a Power BI view with aggregated costs and row-level security limiting each manager to their region. Result: the manager got what was needed and no individual salaries were exposed.
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.
20
Write Python code to extract UTM source, medium and campaign from a column of landing page URLs.
-- A2:D500 holds Order, Customer, Region, Amount
=FILTER(A2:D500, C2:C500="North", "No orders")
-- two conditions (AND) multiply the tests
=FILTER(A2:D500, (C2:C500="North")*(D2:D500>10000), "No orders")
-- OR conditions add the tests
=FILTER(A2:D500, (C2:C500="North")+(C2:C500="East"))
22
What does apply do in pandas and why can it be slow?
apply runs a Python function on each row or column of a DataFrame, or each value of a Series. It is flexible but slow on large data because it loops in Python rather than using optimised C code. For example, df['total'] = df['qty'] * df['price'] is far faster than an apply with a lambda. Prefer vectorised operations, np.where, np.select, map with a dictionary, or string accessor methods first.
23
What steps would you apply in Power Query to clean a messy system export?
Typical steps: remove top junk rows and promote the real header row, remove blank rows and columns, set correct data types, trim and clean text, split combined columns such as Name - City, replace error values, fill down for grouped labels, remove duplicates on the business key, and unpivot wide month columns. Rename steps meaningfully so others can follow them, and keep the query foldable when the source is a database.
Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.
This is an encoding mismatch: a UTF-8 file was read as a different encoding such as Latin-1 or Windows-1252. Re-read it with the right encoding, for example pd.read_csv(path, encoding='utf-8') or 'utf-8-sig' for files with a byte order mark, or set the file origin in Excel and Power Query import. Avoid re-saving through tools that change encoding, and agree on UTF-8 for all exchanges, especially for Hindi or other Indian language text.
26
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.
27
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.
28
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
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.
A Type I error is a false positive: concluding there is an effect when there is none, for example launching a feature that does not actually lift sales. Its probability is alpha, often 0.05. A Type II error is a false negative: missing a real effect, with probability beta. Power is 1 minus beta. Larger samples reduce Type II errors, and the business cost of each mistake should guide which one you guard against more.
30
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
31
What is the difference between marketing mix modelling and multi-touch attribution?
Multi-touch attribution tracks individual user journeys and assigns credit to clicks and views, but it misses offline channels like TV and outdoor, and it is weakened by cookie loss and privacy rules. Marketing mix modelling uses aggregated weekly data, such as spend per channel, sales, price and seasonality, in a regression to estimate each channel's contribution and diminishing returns. MMM is better for budget planning; MTA is better for quick digital optimisation.
32
Describe a time you explained the limits of attribution data to a senior stakeholder.
Situation: a director wanted to cut social ads because last-click reports showed almost no sales. Task: give a fairer view. Action: I showed with simple path examples that social often started journeys that ended in search, compared first-click and data-driven views, and proposed a two-city holdout test. Result: the test showed meaningful incremental sales, so the budget was trimmed modestly instead of cut, and attribution caveats were added to the dashboard.
33
Two tables have a many-to-many relationship and your report totals look inflated. What do you do?
Inflated totals usually mean rows are being double counted through the many-to-many path. I would first check whether a proper dimension table can sit between them, turning it into two one-to-many relationships, for example a bridge table of unique customer IDs. I would set single filter direction, test totals against the source with a simple SQL query, and use measures with DISTINCTCOUNT where needed. Many-to-many should be a deliberate choice, not a default.
34
How would you present the same churn analysis to the CEO and to the customer support team?
For the CEO, lead with the headline and money: churn rose 2 points, costing about 3 crore annually, mainly from one segment, with two recommended actions and their expected impact, all on one or two slides. For the support team, go deeper into drivers they control: ticket types, resolution times and specific customer pain points, with operational next steps and how progress will be tracked. Same data, different level and call to action.
35
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.
36
Tell me about a time you disagreed with your manager about how to measure something.
Situation: my manager wanted to report average resolution time for support tickets. Task: I felt a few very old tickets distorted the average. Action: I prepared a quick comparison showing mean versus median and the percentage resolved within 24 hours, and explained why the median was more representative. Result: we agreed to show median and the 24-hour rate, keeping the mean in a tooltip, and the team stopped chasing misleading spikes.
37
What is statistical significance vs practical significance?
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.
A north star metric is the single measure that best captures the value a product delivers to customers and predicts long-term growth, such as weekly active learners for an education app or orders delivered for a delivery service. It aligns teams around one outcome. It should be supported by input metrics that teams can influence, such as activation rate and repeat usage, and balanced with guardrails like cost or quality.
40
How do you handle a dataset with 30% missing values in a key column?
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.
Tips to crack the EY Data Analytics interview
Practise explaining a data project in business terms, focusing on the problem, approach and recommendation for the client.
Revise Excel, SQL and Power BI, which appear frequently in EY analytics and consulting job descriptions.
Read about EY's service lines and its focus on technology, AI and sustainability consulting for fit questions.
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; EY interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the EY Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); Technical interview; Managerial or case round; 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 EY Data Analytics interview?
This practice set leans on Statistics and Case Studies (8), Power BI (8), Data Cleaning (5), Excel (4). Revise those first, then practise explaining one or two of your own projects end to end, because most EY interviewers ask you to walk through real work.
Are these the exact questions asked at EY?
No. These are curated practice questions that match the skills and difficulty usually tested in consulting interviews like EY's. They are not a leaked or verified list of past questions.
Where does EY hire Data Analytics professionals in India?
EY has large teams in Bengaluru, Kolkata, Gurugram, Hyderabad, Kochi. Openings and locations change often, so check the EY careers page and filter by city and experience.