Walmart Data Analytics Interview Questions & Answers

SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.

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

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

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

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

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

Aggregate revenue by month, use LAG(revenue) OVER (ORDER BY month), then (revenue - prev) / prev x 100.

Joining a table to itself with aliases, useful for hierarchies such as employees and managers or comparing rows within the same table.

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

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.

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

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

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

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

Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.

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

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

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.

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

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

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

ROW_NUMBER gives a unique sequence. RANK gives ties the same rank and skips the next (1, 1, 3). DENSE_RANK gives ties the same rank without gaps (1, 1, 2).

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

Use XLOOKUP or COUNTIF to flag missing items in each list, or merge them in Power Query with a full outer join and filter unmatched rows.

NULL means unknown, so comparisons with = fail; use IS NULL, COALESCE for defaults and remember most aggregates ignore NULLs except COUNT(*).

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.

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.

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.

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.

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.

A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.

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

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

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.

Visually with a box plot, with the IQR rule (below Q1 − 1.5×IQR or above Q3 + 1.5×IQR) or a z-score above 3. Then decide with the business whether to cap, remove or keep them.

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

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.

Use RFM scoring or clustering on behaviour and value, validate segments with business teams and attach an action to each segment.

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.

Row context evaluates one row at a time (calculated columns); filter context is the set of filters from slicers, visuals and relationships that a measure sees.

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

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

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.

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

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

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 extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.

Start with KPIs (revenue, margin, growth), add trend and comparison to target, allow drill-down by region and product, keep it to one screen and test with the audience.

A report is multi-page and interactive from one dataset; a dashboard is a single-page canvas of pinned tiles from many reports in the Service.

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.

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.

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.

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.

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.

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.

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

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