Data Analytics Interview Questions for Architects & Directors (₹40–50 LPA)

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

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

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

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

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.

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 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.

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

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

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.

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

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.

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

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

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

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

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

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

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

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

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

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.

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

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

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

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.

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

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.

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

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

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.

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).

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

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

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.

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

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

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.

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

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

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

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

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

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

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

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.

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

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