Razorpay Data Analytics Interview Questions & Answers

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

Descriptive statistics summarise the data you have; inferential statistics use a sample to draw conclusions about a larger population.

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

Salaries are right-skewed, so the median better represents the typical employee while the mean is pulled up by a few high earners.

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.

Grouping users by a shared start date or trait and tracking behaviour over time, for example monthly retention per signup month.

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

Line for trends, bar for comparisons, scatter for relationships, histogram for distributions and pie only for few parts of a whole.

A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.

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.

COUNT counts numbers, COUNTA counts non-empty cells and COUNTIF counts cells meeting one condition; COUNTIFS handles several conditions.

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.

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

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

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.

A result can be statistically significant yet too small to matter; always report effect size and business impact together with the p-value.

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.

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

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

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

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.

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

A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.

Mean for symmetric data without outliers, median for skewed data (salaries, house prices), mode for categories (the most common city).

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

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

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

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

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

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

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

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.

Find each user's first activity date as a cohort, join to later activity, and compute the share of each cohort active in day, week or month N.

SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions. Example: =SUMIFS(Sales, Region, "North", Month, "Jan")

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.

pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.

A Common Table Expression is a named temporary result defined with WITH that makes complex queries readable and allows recursion.

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.

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.

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.

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

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

Relative references change when copied (A1); absolute references stay fixed ($A$1); mixed references lock only the row or column (A$1 or $A1).

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.

df = pd.read_csv('file.csv'); then head(), info(), describe() and isna().sum() for a quick health check.

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

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.

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.

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

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 bridge that lets the Service refresh data from on-premises sources securely on a schedule.

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.

The population is the whole group of interest; a sample is a subset used to estimate population values.

A primary key uniquely identifies each row in its table; a foreign key references another table's primary key to enforce relationships.

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.

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

Bookmarks save a report view state for navigation; drill-through jumps to a detail page filtered to the item you picked.

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.

It groups rows sharing values so aggregates run per group, for example SELECT region, SUM(sales) FROM orders GROUP BY region.

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