PhonePe Data Analytics Interview Questions & Answers

An ETL tool inside Excel for importing, cleaning and shaping data with recorded steps that refresh automatically when the source changes.

Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.

df['sales'].rolling(window=7).mean() gives a 7-period moving average, useful to smooth daily noise.

INNER JOIN returns only matching rows. LEFT JOIN returns every row from the left table and NULLs where the right table has no match — e.g. all customers, including those with no orders.

Read in chunks with chunksize, load only needed columns, set efficient dtypes or switch to Dask, Polars or a database.

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.

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.

Matplotlib is the low-level plotting library with full control; Seaborn builds on it with attractive statistical charts and simpler syntax.

Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.

Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.

VLOOKUP only looks to the right and breaks when columns are inserted. INDEX-MATCH looks in any direction and is robust. XLOOKUP is the modern option with exact match by default and left lookups.

Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.

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.

melt converts wide data to long format; pivot converts long data to wide, and they are used to reshape data for analysis or charts.

Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.

Pull data with SQL or APIs, clean and aggregate with Pandas, build charts and an Excel or PDF output, and schedule it with cron or Airflow with logging and alerts.

Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.

Start from the business questions, show 4–6 top KPIs with trend vs target, allow drill-downs, keep colours consistent, remove clutter, add time/region filters and give one clear insight per visual.

Organising tables to reduce redundancy and anomalies by splitting data into related tables, commonly up to third normal form.

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.

Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.

Compare with a control group or use difference-in-differences, control for seasonality, and measure incremental sales against campaign cost.

UNION removes duplicate rows and sorts internally; UNION ALL keeps every row and is faster.

It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.

Pick metrics tied to a business goal that are measurable, actionable and owned by someone, and pair a lagging metric with a leading one.

Use TRIM, CLEAN, UPPER or PROPER, Text to Columns, Flash Fill, SUBSTITUTE, and Power Query for repeatable cleaning steps.

WHERE filters rows before grouping. HAVING filters groups after GROUP BY and can use aggregates like SUM() or COUNT().

Create an engine with SQLAlchemy or a driver such as psycopg2, then run pd.read_sql(query, engine) to load results into a DataFrame.

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.

A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.

You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.

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.

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.

Slicers are on-page visual filters users click; filters can be set at visual, page or report level and may be hidden from users.

Profile the data, remove exact duplicates using keys, impute missing values (mean/median/mode or a business rule), flag imputed rows and document every cleaning step.

It automatically formats cells based on rules such as colour scales, data bars or highlighting values above a target, making patterns visible at a glance.

IF returns one result if a test is true and another if false. Nest IFs for multiple outcomes, or use IFS for cleaner formulas.

A clustered index defines the physical order of rows (one per table); a non-clustered index is a separate structure with pointers to the rows.

A Microsoft BI tool made of Power BI Desktop for building reports, the Service for sharing, and Mobile; it connects, models and visualises data.

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 ...).

Indexes speed up lookups by keeping sorted pointers. They slow writes and use space, so index columns used in filters and joins, not every column.

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.

It evaluates an expression after modifying the filter context, for example CALCULATE(SUM(Sales[Amount]), Region[Name] = "North").

df['date'] = pd.to_datetime(df['date']); df['amount'] = pd.to_numeric(df['amount'], errors='coerce'); astype('category') for repeated strings.

SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.

Read the execution plan, add or fix indexes, select only needed columns, filter early, avoid functions on indexed columns, and replace correlated subqueries with joins.

Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.

DAX functions like TOTALYTD, SAMEPERIODLASTYEAR and DATEADD compare periods; they need a proper continuous date table marked as a date table.

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.

DELETE removes chosen rows and can be rolled back; TRUNCATE empties the table quickly; DROP removes the table structure itself.

DAU/MAU, conversion rate, average order value, cart abandonment, retention/churn, CAC, LTV, return rate and NPS.

Detect with IQR or z-score, then cap with clip, remove, transform with log, or keep and flag them depending on whether they are errors or real events.

CALCULATE evaluates an expression in a modified filter context, e.g. CALCULATE([Total Sales], Region[Name] = "North"). It is the key function for time intelligence and comparisons.

Data → Data Validation restricts what can be entered in a cell (lists, number ranges, dates), reducing manual entry errors in shared sheets.

Import loads data into memory for fast visuals; DirectQuery queries the source live, giving fresh data but slower performance and some DAX limits.

SELECT order_date, SUM(amount) OVER (ORDER BY order_date) AS running_total FROM sales;

Check histograms and skew(), then apply log, square-root or Box-Cox transforms, or use medians and robust statistics.

Track users through steps (visit → product view → cart → payment), compute conversion between each step, find the biggest drop, segment by device and source, and suggest fixes to test.

Pandas for data handling, NumPy for numerics, Matplotlib and Seaborn for charts, and openpyxl or SQLAlchemy for files and databases.

Data tab, Remove Duplicates, pick the columns to compare. For a non-destructive way use UNIQUE() or conditional formatting to highlight repeats first.

It depends on margin of error, confidence level and expected variability; larger samples give smaller errors with diminishing returns.

A Common Table Expression (WITH name AS (...)) is a named temporary result used inside one query. Use it to break complex logic into readable steps or for recursive queries.

A pivot table summarises large data by dragging fields into rows, columns, values and filters — for example total sales by region and month — without writing formulas.

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.

LEFT JOIN customers to orders and keep rows where order_id IS NULL, or use NOT EXISTS, which also handles NULLs safely.