36 practice questions with answers for Data Analytics interviews in Bhubaneswar.
Data Analytics jobs in Bhubaneswar
Bhubaneswar is eastern India's emerging IT hub, with campuses of large IT-services companies in Infocity and Info Valley. Freshers make up a big share of hiring, so fundamentals and aptitude rounds matter most.
Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Bhubaneswar.
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01
What is the difference between COUNT(*), COUNT(column) and COUNT(DISTINCT column)?
COUNT(*) counts all rows, including rows with NULLs. COUNT(column) counts only rows where that column is not NULL. COUNT(DISTINCT column) counts unique non-null values. For example, in an orders table, COUNT(*) gives number of orders, COUNT(coupon_code) gives orders that used a coupon, and COUNT(DISTINCT customer_id) gives number of unique buyers. Mixing these up is a common source of wrong KPIs.
Keep a cleaning log or README listing each rule, why it exists and its impact, for example removed 1,240 test orders flagged by email domain. Code-based cleaning in SQL, Python or Power Query steps is itself documentation if steps are named clearly. Record counts before and after each major step. This lets reviewers trust the numbers, helps a colleague rerun the work, and answers the question why your total differs from the raw file.
04
How do you handle large CSV files that do not fit in memory in Python?
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.
06
What is the difference between a dashboard and a report?
I would ask which decisions each new visual supports and how often they will look at it. Usually a few are essential and the rest are nice to have. I would propose keeping the main page focused on core KPIs and moving detail into drill-through pages, tooltips or a separate analysis tab. Showing a quick mock-up of both versions helps them see that the cleaner page answers questions faster.
The Data Model is an in-memory columnar store inside Excel that holds several related tables, and Power Pivot is the interface to manage it. Instead of flattening data with VLOOKUPs, you load fact and dimension tables, create relationships and write DAX measures. It handles millions of rows that would not fit on a sheet, and PivotTables built on it can slice across tables. It is the same engine idea that Power BI uses.
13
What is the difference between Goal Seek and Solver?
Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
14
What are relationships and cardinality in Power BI?
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.
15
What are statistical power and minimum detectable effect (MDE)?
Power is the probability that the test detects a real effect of a given size, commonly set at 80%. The minimum detectable effect is the smallest lift you care about and can reliably detect with your sample size. Smaller MDEs, lower baseline rates and higher power all need more users. If you can only get 10,000 users a week, you may only be able to detect large lifts, which should shape what you test.
An A/A test splits users into two groups that both see the same version. Since nothing differs, you expect no significant difference except about 5% of the time by chance. It checks that randomisation, tracking and the analysis pipeline work correctly, and it reveals sample ratio mismatches or metric variance before real tests depend on the platform. Running many A/A tests also confirms your false positive rate is near 5%.
Tableau Prep is a visual tool for cleaning and shaping data before analysis. You build a flow of steps: connect inputs, clean fields, split columns, group similar values, pivot, join, union and aggregate, and see the data change at every step. The output can be an extract or published data source that dashboards use. It suits analysts who need repeatable preparation without writing SQL or Python.
18
What does Flash Fill do in Excel, and when should you not rely on it?
Flash Fill (Ctrl+E) detects a pattern from one or two examples you type and fills the rest of the column, for example extracting first names or reformatting phone numbers. It is fast for one-time cleanup. However, it produces static values, not formulas, so it will not update when source data changes, and it can guess wrongly on irregular rows. For repeatable reports, use formulas or Power Query instead.
19
Write SQL to find the median order value overall and per city.
-- Works in PostgreSQL, Snowflake, SQL Server (with OVER for SQL Server)
SELECT
city,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY order_value) AS median_value,
AVG(order_value) AS mean_value
FROM orders
GROUP BY city
ORDER BY median_value DESC;
Peeking means checking the p-value repeatedly during a test and stopping as soon as it drops below 0.05. Each look is another chance for random noise to look significant, so the real false positive rate can rise from 5% to 20% or more. Fix it by fixing the sample size in advance and analysing once, or by using methods built for continuous monitoring, such as sequential testing or always-valid confidence intervals.
Remove everything that does not help the message: heavy gridlines, borders, background colour, 3D effects, redundant legends and too many decimal places. Label data directly instead of using a legend, sort bars meaningfully, round numbers, and use one highlight colour with grey for the rest. Shorten axis labels and keep consistent fonts. Ask whether removing each element would lose information; if not, remove it.
22
What is DAX and how does it differ from Excel formulas?
Match refresh to how quickly decisions are made. A monthly board pack needs daily refresh at most, while a warehouse dispatch board may need 15-minute or live updates. Faster refresh costs more in source load, licensing and complexity, and can show incomplete data. Ask how quickly someone will act on a change, check when upstream systems finish loading, and always display the last refreshed time on the dashboard.
24
Write Python code to extract UTM source, medium and campaign from a column of landing page URLs.
Both prevent duplicate values. A table can have only one primary key, it cannot contain NULLs, and it is the main identifier other tables reference through foreign keys. A table can have several unique constraints, for example on email and on PAN number, and most databases allow NULLs in a unique column. Use the primary key for the row identity and unique constraints for other business rules.
A bus matrix is a planning grid with business processes as rows, such as orders, shipments and returns, and conformed dimensions as columns, such as date, customer, product and warehouse. A tick shows which dimensions each process uses. It helps prioritise which fact tables to build, shows which dimensions must be shared and conformed first, and gives leaders a one-page roadmap of the warehouse without technical detail.
27
What are additive, semi-additive and non-additive facts?
Additive facts, like sales amount or quantity, can be summed across every dimension. Semi-additive facts, like account balance or stock on hand, can be summed across accounts or stores but not across time; for time you take the closing or average value. Non-additive facts, like percentages, ratios or unit prices, cannot be summed at all, so you store their components (profit and revenue) and calculate the ratio at query time.
28
What does apply do in pandas and why can it be slow?
apply runs a Python function on each row or column of a DataFrame, or each value of a Series. It is flexible but slow on large data because it loops in Python rather than using optimised C code. For example, df['total'] = df['qty'] * df['price'] is far faster than an apply with a lambda. Prefer vectorised operations, np.where, np.select, map with a dictionary, or string accessor methods first.
Dimensional modelling is a way of designing analytical tables around business processes. Numeric events such as sales or payments go into fact tables, and descriptive context such as customer, product, store and date goes into dimension tables. The design is easy for business users to understand and fast for BI tools to aggregate. For example, a retail model might have one sales fact joined to date, product, store and customer dimensions.
Find out which system or region produced each format, since a source column often explains it. Values with a day above 12 reveal the format unambiguously. Parse with an explicit format per source, for example pd.to_datetime(col, format='%d/%m/%Y', errors='coerce'), rather than letting the tool guess. Check failed or suspicious parses, such as future dates, and ask the source team to send ISO format yyyy-mm-dd going forward.
33
Your test increased conversion rate by 3% but average order value dropped by 5%. What do you recommend?
Look at the metric that reflects business value, such as revenue or margin per visitor, which combines both effects. Here revenue per visitor is roughly 1.03 times 0.95, about 2% lower, so the change may hurt. Check whether the drop is significant, which segments drive it, and whether new buyers are making smaller first orders that grow later. Without a strong reason, I would not ship it as is.
Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.
35
What are variables (VAR) in DAX and why are they useful?
VAR stores the result of an expression inside a measure, and RETURN gives the final output. Variables make measures readable, avoid evaluating the same expression twice, and are evaluated in the filter context where they are defined, which helps avoid subtle bugs inside CALCULATE. They also make debugging easy, because you can temporarily RETURN a variable to check its value before returning the final result.
A correlated subquery refers to a column from the outer query, so it is logically evaluated once per outer row. Example: find employees earning above their department average with WHERE salary > (SELECT AVG(salary) FROM emp e2 WHERE e2.dept_id = e1.dept_id). It is readable but can be slow on large tables. The same result is often faster with a window function such as AVG(salary) OVER (PARTITION BY dept_id).
Few big employers listed in Bhubaneswar?
Many Bhubaneswar candidates also apply to teams in Kolkata and to remote or hybrid roles. Practise the Kolkata set as well.
Which companies hire Data Analytics professionals in Bhubaneswar?
Companies with large offices in Bhubaneswar include many IT services and product firms. Use their company pages below to practise employer-specific sets.
Are Data Analytics interviews in Bhubaneswar different from other cities?
The questions depend more on the company than the city. Bhubaneswar's mix of employers decides the style: Freshers make up a big share of hiring, so fundamentals and aptitude rounds matter most
How do I find Data Analytics jobs in Bhubaneswar?
Shortlist companies with Bhubaneswar offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Bhubaneswar filter, and practise the questions on this page before each interview.