Microsoft Data Analytics Interview Questions & Answers

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.

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

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

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.

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.

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.

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

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

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

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.

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

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

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.

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.

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.

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

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.

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

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

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

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

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.

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

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

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

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.

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.

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

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

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

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

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

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.

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

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

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.

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

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

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

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

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

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.

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

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

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

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

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.

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.

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.

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 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.

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.

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.

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

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

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

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

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

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 ...).

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

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).

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

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.

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.

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