01
What is a self join and when is it used?
Show answer
Joining a table to itself with aliases, useful for hierarchies such as employees and managers or comparing rows within the same table.
Data Analytics Interview Questions in Bangalore
36 practice questions with answers for Data Analytics interviews in Bangalore.
Data Analytics jobs in Bangalore
Bangalore (Bengaluru) is India's largest technology hub, with global product companies, engineering centres, unicorn start-ups and big IT-services campuses. Product-company interviews here are usually deeper, with more scenario, design and hands-on rounds.
Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Bangalore.
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02
What is Row-Level Security?
Show answer
Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.
03
Write SQL to calculate open rate and click-through rate for each email campaign.
Show answer
SELECT
s.campaign_id,
COUNT(DISTINCT s.user_id) AS delivered,
COUNT(DISTINCT o.user_id) AS opened,
COUNT(DISTINCT c.user_id) AS clicked,
ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT s.user_id), 1) AS open_rate,
ROUND(100.0 * COUNT(DISTINCT c.user_id) / COUNT(DISTINCT s.user_id), 1) AS ctr
FROM email_sends s
LEFT JOIN email_opens o ON o.campaign_id = s.campaign_id AND o.user_id = s.user_id
LEFT JOIN email_clicks c ON c.campaign_id = s.campaign_id AND c.user_id = s.user_id
WHERE s.status = 'delivered'
GROUP BY s.campaign_id;
04
How do you decide the sample size for a survey?
Show answer
It depends on margin of error, confidence level and expected variability; larger samples give smaller errors with diminishing returns.
05
Tell me about a time you disagreed with your manager about how to measure something.
Show answer
Situation: my manager wanted to report average resolution time for support tickets. Task: I felt a few very old tickets distorted the average. Action: I prepared a quick comparison showing mean versus median and the percentage resolved within 24 hours, and explained why the median was more representative. Result: we agreed to show median and the 24-hour rate, keeping the mean in a tooltip, and the team stopped chasing misleading spikes.
06
Customer names in your file show strange characters like 'é' instead of accented letters. What is wrong and how do you fix it?
Show answer
This is an encoding mismatch: a UTF-8 file was read as a different encoding such as Latin-1 or Windows-1252. Re-read it with the right encoding, for example pd.read_csv(path, encoding='utf-8') or 'utf-8-sig' for files with a byte order mark, or set the file origin in Excel and Power Query import. Avoid re-saving through tools that change encoding, and agree on UTF-8 for all exchanges, especially for Hindi or other Indian language text.
07
When would you choose Polars or DuckDB over pandas?
Show answer
Choose them when data is large, roughly several gigabytes, or when pandas is slow or runs out of memory. DuckDB runs fast SQL directly on CSV and Parquet files on a laptop, ideal for analysts comfortable with SQL. Polars is a DataFrame library with lazy evaluation and multi-threading, often many times faster than pandas. pandas still wins for small data, its huge ecosystem and compatibility with most tutorials and libraries.
08
How do you calculate working days between two dates in Excel, excluding holidays?
Show answer
-- A2 = start date, B2 = end date, Holidays = named range of holiday dates
=NETWORKDAYS(A2, B2, Holidays)
-- if the weekend is only Sunday (weekend code 11)
=NETWORKDAYS.INTL(A2, B2, 11, Holidays)
-- date that is 10 working days after A2
=WORKDAY(A2, 10, Holidays)
09
Difference between UNION and UNION ALL?
Show answer
UNION removes duplicate rows and sorts internally; UNION ALL keeps every row and is faster.
10
What are field parameters in Power BI?
Show answer
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
11
What is a holdout group and why do teams keep one?
Show answer
A holdout group is a small share of users, often 5-10%, kept out of a feature or of all marketing campaigns for a long time. Comparing them with everyone else shows the cumulative long-term impact, which individual short tests miss, and checks whether many small wins really add up. For example, a notification holdout shows whether push messages raise monthly retention or just shift activity earlier.
12
What is the peeking problem in A/B testing?
Show answer
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.
13
What is the difference between a fact table and a dimension table?
Show answer
A fact table stores measurable events at a defined grain, such as one row per order line, with numeric measures like quantity and amount plus foreign keys. Dimension tables describe the context of those events: customer, product, store and date, with descriptive attributes used for filtering and grouping. Facts are long and grow quickly; dimensions are wider and smaller. Together they form star or snowflake schemas.
14
What is a good structure for a data story?
Show answer
A simple structure is context, conflict, resolution. Context sets up what the audience already knows, such as our target was 10 percent growth. Conflict shows the surprising finding or problem, such as growth is only 3 percent because repeat buyers dropped. Resolution gives the recommendation and expected impact. Another option is situation, complication, question, answer. Either way, the audience should know the main message within the first minute.
15
What is a bridge table and when do you need one?
Show answer
A bridge table resolves a many-to-many relationship between a fact and a dimension. For example, one bank account can have several holders and one customer can hold several accounts, so a bridge table stores account_key, customer_key and often an allocation weight. Without it, joining facts to customers duplicates amounts. Use weights to split balances, or report clearly that totals across customers will exceed the true total.
16
What is the difference between a calculated field and a helper column in Excel?
Show answer
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.
17
What is a junk dimension?
Show answer
A junk dimension combines several low-cardinality flags and indicators, such as payment type, is_gift, is_express and order channel, into one small dimension table. Each row is a unique combination of those values, and the fact table stores a single key to it. This keeps the fact table narrow and avoids many tiny dimensions or loose flag columns. It works best when the combinations are limited, typically a few hundred rows.
18
How do you handle a dataset with 30% missing values in a key column?
Show answer
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.
19
Write SQL to show each category's percentage share of total revenue.
Show answer
SELECT category,
SUM(amount) AS revenue,
ROUND(100.0 * SUM(amount)
/ SUM(SUM(amount)) OVER (), 2) AS pct_share
FROM sales
GROUP BY category
ORDER BY revenue DESC;
-- SUM(SUM(amount)) OVER () is the grand total across all groups
20
Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?
Show answer
A context filter is applied before other dimension filters and before FIXED LOD calculations, creating a smaller dataset the rest of the view works on. Use it when a Top N filter should operate within a selected category, for example top 10 products within Electronics, or when a FIXED LOD must respect a filter. Add a filter to context from its right-click menu; it turns grey. Too many context filters can hurt performance.
21
Write pandas code to compute month-on-month percentage change and cumulative sales for each store.
Show answer
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
22
What is the difference between an outlier and a data error?
Show answer
An outlier is a genuine but unusual value, such as a bulk corporate order of 50 laptops; it is real and may be important. A data error is an incorrect value, such as an age of 250 or a price entered in paise instead of rupees. Errors should be fixed or removed. Outliers should be investigated and often kept, perhaps analysed separately or with median-based metrics so they do not distort averages.
23
What are absolute and relative cell references?
Show answer
Relative references change when copied (A1); absolute references stay fixed ($A$1); mixed references lock only the row or column (A$1 or $A1).
24
How should you document data cleaning decisions?
Show answer
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.
25
Explain correlation vs regression.
Show answer
Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.
26
How do you remove duplicates in Excel but keep the latest record?
Show answer
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.
27
A manager asks for a dashboard that refreshes every minute. How do you decide the right refresh frequency?
Show answer
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.
28
What is the difference between OLTP and OLAP systems?
Show answer
OLTP systems run day-to-day operations, such as billing or order entry. They handle many small inserts and updates, use normalised tables and need fast single-row access. OLAP systems support analysis, scanning millions of rows to aggregate sales by month or region. They use denormalised star schemas, columnar storage and are mostly read-heavy. Analysts should query OLAP copies so heavy reports do not slow down the live application.
29
Write a Tableau calculated field for profit ratio and a profit category.
Show answer
// Profit Ratio (aggregate first, then divide)
SUM([Profit]) / SUM([Sales])
// Profit Category
IF SUM([Profit]) < 0 THEN 'Loss'
ELSEIF [Profit Ratio] < 0.1 THEN 'Low margin'
ELSE 'Healthy'
END
// avoid AVG([Profit]/[Sales]): it averages row ratios and gives a wrong total
30
What is a story in Tableau?
Show answer
A story is a sequence of sheets or dashboards, called story points, that walk an audience through an analysis step by step, like slides. Each point can keep its own filters and selections and has a caption explaining the insight. It is useful for presenting findings such as why churn rose, moving from the headline number to segments and recommendations. For everyday monitoring, a regular dashboard is better than a story.
31
What is the difference between ETL and ELT?
Show answer
In ETL, data is extracted from sources, transformed on a separate engine and then loaded into the warehouse. In ELT, raw data is loaded first and transformed inside the warehouse using SQL, often with a tool like dbt. Cloud warehouses with cheap storage and scalable compute made ELT the common choice in 2026, because raw data stays available for reprocessing. ETL still suits cases needing masking or heavy cleansing before data lands.
32
Organic search traffic fell 40% overnight. How do you investigate?
Show answer
First check whether it is a tracking problem: did the analytics tag break on some templates, did consent settings change, does server log or backend order data show the same fall? If the drop is real, check the search console for impressions and rankings, recent site releases such as changed URLs, missing redirects, noindex tags or robots.txt changes, and whether a search algorithm update or seasonal pattern matches. Then break the drop down by page group.
33
Tell me about a time a dashboard you built changed a business decision.
Show answer
Situation: our operations team assumed late deliveries were evenly spread. Task: I built a delivery performance dashboard. Action: I added a breakdown by courier partner and pin code cluster with a weekly trend, and reviewed it with the operations manager. Result: it showed one partner caused 60 percent of delays in two zones. The company shifted those zones to another partner, and on-time delivery rose from 84 to 92 percent in two months.
34
What is the difference between a surrogate key and a natural key?
Show answer
A natural key comes from the source system and has business meaning, such as a customer mobile number or product SKU. A surrogate key is a meaningless integer or hash generated in the warehouse. Surrogate keys protect the model when source keys change, get reused or clash across systems, and they are essential for slowly changing dimensions where one customer has several historical rows. Keep the natural key as an attribute for lookups.
35
Tell me about a time you used data to challenge an assumption.
Show answer
Situation: our team believed weekend discounts drove most sales. Task: I was preparing the monthly review. Action: I compared discount and non-discount weekends over six months and controlled for paydays and festivals. Discounts moved sales forward from the following week but added little overall. I presented it with one clear chart. Result: management trialled fewer discount weekends, and margin improved without a fall in monthly revenue.
36
How do you change data types and parse dates?
Show answer
df['date'] = pd.to_datetime(df['date']); df['amount'] = pd.to_numeric(df['amount'], errors='coerce'); astype('category') for repeated strings.
Companies with offices in Bangalore (134)
IT services
TCS
Data Analytics interview questions
Mumbai, Chennai, Pune +2
View questionsIT services
Infosys
Data Analytics interview questions
Bengaluru, Mysuru, Pune +2
View questionsIT services
Wipro
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Bengaluru, Hyderabad, Pune +2
View questionsConsulting
Accenture
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Bengaluru, Hyderabad, Pune +2
View questionsIT services
Cognizant
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Chennai, Bengaluru, Pune +2
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Capgemini
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Mumbai, Bengaluru, Pune +2
View questionsConsulting
Deloitte
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Hyderabad, Bengaluru, Mumbai +2
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Amazon
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Bengaluru, Hyderabad, Chennai +2
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Bengaluru, Hyderabad, Gurugram +2
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Meta
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Gurugram, Bengaluru, Hyderabad +1
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Microsoft
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Hyderabad, Bengaluru, Noida +2
View questionsHardware & semiconductors
Apple
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Hyderabad, Bengaluru, Mumbai +1
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Adobe
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Noida, Bengaluru, Gurugram
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Oracle
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Bengaluru, Hyderabad, Noida +2
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Salesforce
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Hyderabad, Bengaluru, Mumbai +1
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ServiceNow
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Hyderabad, Bengaluru
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Atlassian
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Bengaluru
View questionsFintech
Intuit
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Bengaluru
View questionsHardware & semiconductors
Cisco
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Bengaluru, Pune, Chennai
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Data Analytics interview questions
Bengaluru, Gurugram, Mumbai
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Uber
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Bengaluru, Hyderabad, Gurugram
View questionsE-commerce & consumer tech
Walmart
Data Analytics interview questions
Bengaluru, Chennai, Gurugram
View questionsE-commerce & consumer tech
Flipkart
Data Analytics interview questions
Bengaluru, Hyderabad
View questionsE-commerce & consumer tech
Swiggy
Data Analytics interview questions
Bengaluru, Hyderabad
View questionsFrequently asked questions
Which companies hire Data Analytics professionals in Bangalore?
Companies with large offices in Bangalore include TCS, Infosys, Wipro, Accenture, Cognizant, Capgemini, Deloitte, Amazon, Google, Meta. Use their company pages below to practise employer-specific sets.
Are Data Analytics interviews in Bangalore different from other cities?
The questions depend more on the company than the city. Bangalore's mix of employers decides the style: Product-company interviews here are usually deeper, with more scenario, design and hands-on rounds
How do I find Data Analytics jobs in Bangalore?
Shortlist companies with Bangalore offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Bangalore filter, and practise the questions on this page before each interview.
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