Data Analytics Interview Questions for Associate Roles (₹6–8 LPA)

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.

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.

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

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

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).

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.

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

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

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

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

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.

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

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

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.

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

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

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.

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

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

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.

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

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.

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

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

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.

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

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.

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

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

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.

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

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

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

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

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

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

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.

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

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

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.

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

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

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.

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.

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

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

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.