Meta Data Analytics Interview Questions & Answers

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

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

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

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

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.

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

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.

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

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.

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.

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.

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

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

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.

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

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

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

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.

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

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

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

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

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.

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

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

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

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.

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

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.

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.

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

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).

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

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

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

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.

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

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

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

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

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.

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

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.

DAX is the formula language for Power BI models. It works on columns and tables with filter context rather than on individual cells.

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

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

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

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.

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

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.

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

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

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

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

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

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

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

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 ...).

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

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 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.

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

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

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.

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

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

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.

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

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

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

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

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

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").