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Data Analytics Rapid-Fire Questions & Answers

50 questions for the rapid-fire stage — 33 easy, 17 medium.

About the rapid-fire round

Rapid-fire questions are quick definitions and one-line answers that interviewers fire at the start or end of a round to check your Data Analytics basics. Aim to answer each in under 20 seconds.

Tips for the rapid-fire round

  • Answer in one or two sentences, then stop.
  • If you don't know, say so quickly and move on; don't guess at length.
  • Revise these the morning of the interview.
  • Practise with a friend who reads them in random order.
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01

What do CTR, CPC, CPA and ROAS mean in digital marketing?

Web & Marketing AnalyticsEasy
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CTR, click-through rate, is clicks divided by impressions. CPC, cost per click, is spend divided by clicks. CPA, cost per acquisition, is spend divided by conversions such as orders or leads. ROAS, return on ad spend, is revenue from ads divided by ad spend, so a ROAS of 4 means 4 rupees of revenue per rupee spent. ROAS ignores product margin, so a high ROAS can still lose money.
02

What is the Central Limit Theorem and why does it matter for analysts?

Statistics and Case StudiesMedium
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The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, usually from about 30 observations, even if the underlying data is skewed. This is why we can use z-tests, t-tests and confidence intervals for averages such as order value or session time. It does not make the raw data normal, and very heavy-tailed data may need larger samples.
03

What is DAX and how does it differ from Excel formulas?

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DAX is the formula language for Power BI models. It works on columns and tables with filter context rather than on individual cells.
04

INNER JOIN vs LEFT JOIN — with an example.

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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.
05

What is columnar storage and why do analytical warehouses use it?

Data Modelling & WarehousingMedium
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Columnar storage keeps all values of one column together on disk instead of storing full rows together. Analytical queries usually read a few columns across millions of rows, so the engine reads only those columns. Similar values sit side by side and compress very well, which cuts storage and scan cost. The trade-off is slower single-row inserts and updates, which is why OLTP databases stay row-based.
06

What is hypothesis testing in simple words?

Statistics and Case StudiesMedium
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You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.
07

Difference between UNION and UNION ALL?

SQLMedium
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UNION removes duplicate rows and sorts internally; UNION ALL keeps every row and is faster.
08

What do COALESCE and NULLIF do in SQL?

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COALESCE returns the first non-null value from its arguments, for example COALESCE(phone, alt_phone, 'Not given') fills missing phone numbers. NULLIF(a, b) returns NULL when a equals b, otherwise a. Its classic use is avoiding division by zero: revenue / NULLIF(orders, 0) returns NULL instead of an error when orders is zero. Together they handle most null-related reporting problems cleanly.
09

What is normalisation?

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Organising tables to reduce redundancy and anomalies by splitting data into related tables, commonly up to third normal form.
10

What are novelty and primacy effects in experiments?

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A novelty effect is when users engage more with a change simply because it is new, so the early lift fades over time. A primacy effect is the opposite: existing users resist a change at first because they are used to the old design, so results start poor and improve. Both mean short tests mislead. Run tests for at least one or two full weeks and compare new versus returning users.
11

What is the difference between dimensions and measures in Tableau?

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Dimensions are qualitative fields such as region, product or date that slice data into groups and create headers. Measures are quantitative fields such as sales or profit that get aggregated, by default with SUM. Dragging a dimension to rows creates one row per member; dragging a measure creates an axis. A numeric field like customer ID should be converted to a dimension so it is not summed.
12

What is the difference between a clustered and a non-clustered index?

SQLMedium
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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.
13

Mean vs median vs mode — when do you use which?

Statistics and Case StudiesEasy
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Mean for symmetric data without outliers, median for skewed data (salaries, house prices), mode for categories (the most common city).
14

What is cohort analysis?

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Grouping users by a shared start date or trait and tracking behaviour over time, for example monthly retention per signup month.
15

What is a lambda function in Python and where do analysts use it?

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A lambda is a small anonymous function written in one line, for example lambda x: x * 1.18 to add GST. Analysts use lambdas with sorted for custom sort keys, with pandas assign for computed columns, for example assign(margin=lambda d: d.profit / d.sales), and occasionally with apply. For anything longer than one expression or reused in several places, a normal def function is clearer and easier to test.
16

What is the difference between an A/B test, an A/B/n test and a multivariate test?

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An A/B test compares one change with control. An A/B/n test compares several versions of one element, such as three headlines, with control. A multivariate test changes several elements at once, such as headline and button colour, and tests every combination to see each element's effect and their interactions. Multivariate tests need much more traffic because users are spread across many combinations.
17

What is a data mart?

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A data mart is a subset of the warehouse built for one department or subject, such as sales, finance or HR. It contains the facts and dimensions that team needs, often pre-aggregated and with business-friendly names. Marts make reporting faster and simpler for that team. Ideally they are built from the central warehouse using conformed dimensions, so the finance mart and the sales mart still agree on revenue.
18

What does CASE WHEN do?

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It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.
19

What is a PivotTable and when do you use it?

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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.
20

When would you use a one-tailed test instead of a two-tailed test?

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A two-tailed test checks for a difference in either direction and is the safer default for A/B tests. A one-tailed test checks only one direction, such as treatment being better, and needs fewer users. Use it only if you decided beforehand that you would act the same way whether the change has no effect or a negative effect. Otherwise you may miss harm, which is why most teams stay two-tailed.
21

What are statistical power and minimum detectable effect (MDE)?

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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.
22

What is the difference between DELETE, TRUNCATE and DROP?

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DELETE removes chosen rows and can be rolled back; TRUNCATE empties the table quickly; DROP removes the table structure itself.
23

Explain correlation vs regression.

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Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.
24

What is the difference between a metric and a KPI?

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A metric is any quantity you measure, such as page views, tickets closed or average order value. A KPI, key performance indicator, is a metric chosen because it directly reflects progress towards an important business goal and usually has a target and owner. Every KPI is a metric, but most metrics are not KPIs. A good dashboard leads with a few KPIs and keeps supporting metrics for diagnosis.
25

What is tidy data?

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Tidy data follows three rules: each variable is a column, each observation is a row, and each type of observational unit is a table. A sheet with months as columns is not tidy; unpivoting it to Region, Month and Sales columns makes it tidy. Tidy data is easy to filter, group, join and chart in pandas, SQL, Power BI and Tableau, so reshaping into this form is a core cleaning step.
26

What is data storytelling?

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Data storytelling is communicating insights by combining three things: data, the evidence; visuals, charts that make patterns obvious; and narrative, the context and so-what that explains why it matters and what to do. A table of numbers informs, but a story persuades someone to act. For an analyst, it means leading with the key finding, showing just enough evidence, and ending with a clear recommendation.
27

What is regression to the mean? Give a business example.

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Regression to the mean means extreme results tend to be followed by more average results, simply because part of the extreme was luck. For example, if you pick the 10 worst-performing stores this quarter and give them a training programme, many will improve next quarter even without training. To claim the programme worked, compare them with similar low performers who did not get it.
28

What is a holdout group and why do teams keep one?

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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.
29

What is Tableau Prep used for?

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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.
30

What is a primary key vs a foreign key?

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A primary key uniquely identifies each row in its table; a foreign key references another table's primary key to enforce relationships.
31

What is descriptive vs inferential statistics?

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Descriptive statistics summarise the data you have; inferential statistics use a sample to draw conclusions about a larger population.
32

What is the difference between pd.cut and pd.qcut?

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pd.cut bins values into intervals you define or into equal-width ranges, for example age groups 18-25, 26-35 and 36-50. pd.qcut bins by quantiles so each bin has roughly the same number of rows, for example dividing customers into spend quartiles. Use cut when business-defined boundaries matter and qcut when you want balanced groups such as deciles. Both return a categorical column you can group by.
33

What is data validation in Excel?

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Data → Data Validation restricts what can be entered in a cell (lists, number ranges, dates), reducing manual entry errors in shared sheets.
34

What is the difference between concat, merge and join in pandas?

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concat stacks DataFrames vertically (more rows) or side by side along an axis, for example appending monthly files with the same columns. merge combines DataFrames on key columns, like a SQL join, with how='inner', 'left', 'right' or 'outer'. join is a shortcut for merging on the index. Use concat to append and merge to look up related data. Always check row counts after a merge.
35

Which Python libraries do analysts use most?

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Pandas for data handling, NumPy for numerics, Matplotlib and Seaborn for charts, and openpyxl or SQLAlchemy for files and databases.
36

Why does correlation not imply causation? Give a business example.

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Two variables can move together because of a third factor, reverse causation or pure chance. Example: stores with more staff have higher sales, but busy locations get more staff because they already sell more, so hiring alone may not raise sales. Ice-cream sales and AC sales both rise in summer because of temperature. To claim causation you need experiments, natural experiments or careful control of confounders.
37

What is a pivot table and when do you use it?

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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.
38

What are named ranges in Excel and why are they useful?

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A named range gives a cell or range a meaningful name, such as GST_Rate or SalesData, managed from the Name Manager. Formulas become readable, for example =Amount*GST_Rate instead of =B2*$H$1, and they do not break when you move the input cell. Names also work as data validation sources and in charts. For growing data, prefer Excel Tables, whose structured references expand automatically.
39

How do you choose the right chart?

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Line for trends, bar for comparisons, scatter for relationships, histogram for distributions and pie only for few parts of a whole.
40

What is the logical order in which SQL executes a SELECT query?

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Although written as SELECT first, a query is logically processed as FROM and JOIN, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because SELECT runs later, but you can use it in ORDER BY. It also explains why aggregates belong in HAVING, not WHERE, and why window functions cannot be filtered in the same query level.
41

What is vectorisation and why does it matter?

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Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.
42

What is the difference between a view and a materialised view?

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A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.
43

What is the difference between COUNT(*), COUNT(column) and COUNT(DISTINCT column)?

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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.
44

What is the difference between loc and iloc in pandas?

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loc selects by labels: row index labels and column names, for example df.loc[df['city'] == 'Pune', ['name', 'sales']]. iloc selects by integer position, for example df.iloc[0:5, 0:3] for the first five rows and three columns. With loc, slices include the end label; with iloc, the end position is excluded like normal Python. Use loc for readable filtering and iloc when position matters.
45

What are absolute and relative cell references?

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Relative references change when copied (A1); absolute references stay fixed ($A$1); mixed references lock only the row or column (A$1 or $A1).
46

What are slicers and filters?

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Slicers are on-page visual filters users click; filters can be set at visual, page or report level and may be hidden from users.
47

What is the difference between users, sessions and events in Google Analytics 4?

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GA4 uses an event-based model: every interaction, such as page_view, scroll, add_to_cart or purchase, is an event with parameters. A session is a group of events from one user within a period of activity, ending after 30 minutes of inactivity by default. A user is identified by a device cookie, app instance or a logged-in user ID. One user can have many sessions, and each session many events.
48

What is a date table in Power BI and why should you mark it as one?

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A date table has one row per day with no gaps, covering the full range of your data, plus columns like year, month, quarter and week. Marking it as a date table tells Power BI to use it for time intelligence functions such as TOTALYTD and SAMEPERIODLASTYEAR. Without it, those functions can give wrong results and auto date tables bloat the model. Relate it to every fact table's date column.
49

What is RAG status and how should thresholds be set?

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RAG means red, amber, green, a traffic-light status that shows at a glance whether a KPI is on track. Thresholds should be agreed with the business owner in advance, for example green at or above 100 percent of target, amber from 90 to 99 percent, red below 90 percent. Do not rely only on colour; add icons or text for colour-blind users, and avoid so many reds that nobody reacts.
50

How are NULLs handled in SQL?

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

Frequently asked questions

What happens in the Data Analytics rapid-fire round?

Rapid-fire questions are quick definitions and one-line answers that interviewers fire at the start or end of a round to check your Data Analytics basics. Aim to answer each in under 20 seconds.

How do I clear the rapid-fire round?

Answer in one or two sentences, then stop. If you don't know, say so quickly and move on; don't guess at length. Revise these the morning of the interview.

Which round comes next?

A typical order is online assessment, telephonic screening, one or two technical rounds, a managerial round and HR. Use the other round pages to practise each stage.

Practise every interview round

Online Assessment questionsTelephonic Interview questionsTechnical Round questionsManagerial Round questionsHR & behavioural questionsFull mock interview

More ways to practise

Company-wise Data Analytics questionsPackage-wise Data Analytics questionsExperience-wise Data Analytics questionsRole-wise Data Analytics questionsCity-wise Data Analytics questionsMost asked Data Analytics questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsData Analytics mock interviewOnline assessment questionsTechnical round questionsManagerial round questionsData Analytics topic sheets