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Data Analytics Online Assessment Questions & Answers

40 questions for the online assessment stage — 14 easy, 25 medium, 1 hard.

About the online assessment round

Most companies start with an online test: aptitude and reasoning, a few MCQs on core Data Analytics concepts, and one or two short hands-on tasks. It filters a large number of applicants, so speed and accuracy both count.

Tips for the online assessment round

  • Practise under a timer: about one minute per MCQ and 15-20 minutes per hands-on task.
  • Read every option before answering; tests often include two nearly correct choices.
  • Use a stable internet connection, keep your camera on and avoid switching tabs in proctored tests.
  • Attempt the questions you are sure about first, then return to the harder ones.
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01

Write Excel formulas to get a sorted list of unique customer names and their count.

ExcelEasy
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-- unique customers from column B, sorted A to Z =SORT(UNIQUE(B2:B1000)) -- number of unique customers =COUNTA(UNIQUE(B2:B1000)) -- unique customers sorted by total sales, highest first =SORTBY(UNIQUE(B2:B1000), SUMIFS(D2:D1000, B2:B1000, UNIQUE(B2:B1000)), -1)
02

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

A/B Testing & ExperimentsEasy
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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.
03

What is a gateway in Power BI?

Power BIMedium
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A bridge that lets the Service refresh data from on-premises sources securely on a schedule.
04

What is the difference between bounce rate and engagement rate in GA4?

Web & Marketing AnalyticsEasy
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In GA4, an engaged session lasts longer than 10 seconds, has a conversion (key event) or has at least two page or screen views. Engagement rate is engaged sessions divided by total sessions. Bounce rate is simply the opposite: the share of sessions that were not engaged. This is more useful than the old single-page bounce definition, because a user who reads a full blog post for three minutes is no longer counted as a bounce.
05

How should you lay out a dashboard page?

Dashboards and KPIsEasy
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Readers scan from the top-left, so place the most important KPIs as cards across the top, then trend charts, then breakdowns and detail tables at the bottom. Group related visuals, align them on a grid and keep consistent spacing, fonts and colours. Put filters in one consistent place. Limit a page to roughly five to eight visuals that answer related questions, and give the page a clear title stating its purpose.
06

How do you handle null values in Tableau calculations?

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// treat missing sales as zero ZN(SUM([Sales])) // replace a missing value with a default IFNULL([Region], 'Unknown') // test explicitly IF ISNULL([Discount]) THEN 0 ELSE [Discount] END // right-click a null indicator on an axis to filter or show nulls at default position
07

What is the grain of a fact table and why should you decide it first?

Data Modelling & WarehousingMedium
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The grain is the exact meaning of one row in a fact table, such as one order line, one daily store total or one monthly account balance. You decide it first because it controls which dimensions can join, which measures are valid and how totals behave. Mixing grains in one table, for example order lines and order-level shipping charges, causes double counting. Always state the grain in one sentence before adding columns.
08

What is an action title and why should you use one?

Data StorytellingMedium
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An action title states the insight of a chart or slide as a sentence instead of a label. Instead of Monthly Sales by Region, write South region drove 70 percent of the decline since July. Busy executives may read only the titles, so each one should carry the message and, ideally, the so-what. The chart below then proves the title. Read only your titles in order: they should tell the whole story.
09

Write a DAX measure for a KPI card showing achievement against target and a colour for conditional formatting.

Dashboards and KPIsMedium
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Achievement % = DIVIDE([Total Sales], [Sales Target]) KPI Colour = VAR a = [Achievement %] RETURN SWITCH( TRUE(), ISBLANK(a), "#9E9E9E", a >= 1, "#2E7D32", a >= 0.9, "#F9A825", "#C62828" ) -- apply KPI Colour via Format > Callout value > fx > Field value
10

What is the difference between Goal Seek and Solver?

ExcelMedium
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Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
11

What is a self join and when is it used?

SQLMedium
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Joining a table to itself with aliases, useful for hierarchies such as employees and managers or comparing rows within the same table.
12

How do you calculate a running total?

SQLMedium
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SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.
13

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

Power BIMedium
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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.
14

What questions do you ask a stakeholder before building a dashboard?

Dashboards and KPIsMedium
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Who will use it and what decisions will they make with it? Which three to five questions must it answer? What are the KPI definitions, targets and comparison periods? Which data sources are trusted? How often will they look at it, and on what device? Who needs access and are there security restrictions? What does success look like in a month? I summarise the answers in a short brief and get sign-off before building.
15

How do SUMIFS and SUMPRODUCT differ?

ExcelMedium
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SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
16

How do you connect Python to a SQL database?

Python for AnalysisMedium
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Create an engine with SQLAlchemy or a driver such as psycopg2, then run pd.read_sql(query, engine) to load results into a DataFrame.
17

Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.

Python for AnalysisMedium
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df['date'] = pd.to_datetime(df['date']) daily = (df.groupby('date')['sales'].sum() .asfreq('D', fill_value=0)) # adds missing days as 0 weekly = daily.resample('W-MON', label='left', closed='left').sum() print(weekly.head()) # W-MON with label/closed left gives weeks starting on Monday
18

Where should data cleaning happen: Excel, SQL, Python or Power Query?

Data CleaningHard
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Clean as close to the source as is practical and in a repeatable tool. Fixes everyone needs belong upstream in the database or warehouse via SQL so all reports share them. Power Query suits report-specific shaping and Excel users. Python is best for complex logic like fuzzy matching or regex at scale. Manual Excel edits are fine for one-off exploration but should not be part of recurring reports because they cannot be reproduced.
19

What is the difference between a metric and a KPI?

Dashboards and KPIsEasy
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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.
20

Your findings deck has 30 slides and you have 10 minutes with leadership. How do you cut it down?

Data StorytellingMedium
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Write the single key message first, then keep only slides that directly support it, usually three to five: the headline finding, the main driver, the impact in money, and the recommendation with next steps. Merge related charts, use action titles, and move everything else to an appendix for questions. Rehearse to fit in about seven minutes, leaving time for discussion, because the discussion is where decisions are made.
21

What is a role-playing dimension?

Data Modelling & WarehousingMedium
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A role-playing dimension is one physical dimension used several times by the same fact table in different roles. The classic case is the date dimension: an orders fact has order date, ship date and delivery date keys, all pointing to the same date table. In SQL you join it three times with different aliases; in Power BI you keep one active relationship and use USERELATIONSHIP or create separate date views for each role.
22

How do you remove duplicates in Excel?

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Data tab, Remove Duplicates, pick the columns to compare. For a non-destructive way use UNIQUE() or conditional formatting to highlight repeats first.
23

What is Row-Level Security?

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Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.
24

Traffic suddenly doubled from one city, with near-zero engagement time and no conversions. What is happening and what do you do?

Web & Marketing AnalyticsMedium
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This is very likely bot or spam traffic, or a misfiring tag. Check the user agents, IP ranges, hostnames, screen resolutions and landing pages for that traffic. If it is bots, exclude it with filters or data segments in analytics and in your reporting layer, and inform the web team so they can block it. Then restate the affected dashboard numbers, because bots inflate sessions and pull down conversion rate.
25

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.
26

What is conditional formatting?

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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.
27

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.
28

Write a DAX measure for cumulative sales from the start of the data to the selected date.

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Cumulative Sales = VAR maxDate = MAX('Date'[Date]) RETURN CALCULATE( [Total Sales], FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate) ) -- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
29

How do you choose the right chart?

Data StorytellingEasy
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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.
30

How can a chart mislead even when the numbers are correct?

Data StorytellingEasy
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Common tricks are a truncated y-axis on bar charts, which exaggerates small differences; inconsistent scales across side-by-side charts; dual axes chosen to imply a correlation; cherry-picked time windows; cumulative charts that always rise; and area or 3D effects that distort size. Bars should start at zero, while line charts may zoom in if clearly labelled. Always check that the visual impression matches the actual magnitude.
31

Your event logs store timestamps in UTC, but the business reports daily sales in IST, and late-night orders land on the wrong day. How do you fix it?

Data CleaningMedium
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Convert timestamps to the business time zone before deriving the date. IST is UTC plus 5 hours 30 minutes, so an order at 20:00 UTC belongs to the next day in IST. In SQL use AT TIME ZONE or CONVERT_TZ, and in pandas use tz_localize('UTC') followed by tz_convert('Asia/Kolkata'). Store raw UTC, add a local date column in the model, and document which one each report uses.
32

Write pandas code to standardise inconsistent city names such as 'delhi ', 'New Delhi' and 'DELHI'.

Data CleaningMedium
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city_map = { 'delhi': 'Delhi', 'new delhi': 'Delhi', 'bombay': 'Mumbai', 'mumbai': 'Mumbai', 'bengaluru': 'Bengaluru', 'bangalore': 'Bengaluru', } clean = df['city'].str.strip().str.lower().str.replace(r'\s+', ' ', regex=True) df['city_clean'] = clean.map(city_map).fillna(clean.str.title()) print(df['city_clean'].value_counts())
33

Write a DAX measure for year-over-year sales growth percentage.

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Sales LY = CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date])) Sales YoY % = VAR cur = [Total Sales] VAR prev = [Sales LY] RETURN DIVIDE(cur - prev, prev) -- needs a marked date table; format the measure as a percentage
34

What is a p-value, explained simply?

Statistics and Case StudiesMedium
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A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis, usually no effect, were true. A p-value of 0.03 means that if the new page truly made no difference, results this strong would appear only about 3 percent of the time by chance. Below a chosen threshold such as 0.05 we reject the null. It is not the probability that the hypothesis is true.
35

Analytics shows 1,820 purchases last month but the order database shows 2,150. Which is right, and how do you explain the gap?

Web & Marketing AnalyticsMedium
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The order database is the source of truth for counts and revenue. Analytics usually undercounts because of ad blockers, declined cookie consent, users closing the page before the confirmation tag fires, and payment redirects that break sessions. It can also overcount from duplicate tags on page refresh. Quantify the gap per month; if it is stable around 15%, use analytics for channel mix and trends and the database for totals.
36

How do you clean messy text data in Excel?

Data CleaningMedium
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Use TRIM, CLEAN, UPPER or PROPER, Text to Columns, Flash Fill, SUBSTITUTE, and Power Query for repeatable cleaning steps.
37

Numbers in an Excel column are stored as text and SUM returns zero. How do you fix it?

Data CleaningEasy
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Signs are left-aligned numbers, green triangles, and COUNT returning less than COUNTA. Fix options: select the column and use the warning menu Convert to Number; use Data, Text to Columns and Finish; or a helper column with =VALUE(TRIM(A2)). If there are hidden characters, use =VALUE(SUBSTITUTE(TRIM(A2), CHAR(160), "")) to remove non-breaking spaces from web exports. In Power Query, simply change the column type to number.
38

Write SQL to calculate users, conversions and conversion rate for each test variant.

A/B Testing & ExperimentsEasy
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SELECT a.variant, COUNT(DISTINCT a.user_id) AS users, COUNT(DISTINCT o.user_id) AS converters, ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT a.user_id), 2) AS conversion_pct FROM experiment_assignments a LEFT JOIN orders o ON o.user_id = a.user_id AND o.order_ts >= a.assigned_ts WHERE a.experiment_id = 'checkout_v2' GROUP BY a.variant;
39

Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).

ExcelMedium
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-- C2 = invoice date, TODAY() = as-of date =LET(age, TODAY()-C2, IFS(age<=30, "0-30", age<=60, "31-60", age<=90, "61-90", TRUE, "90+")) -- then a PivotTable with bucket in rows and SUM of amount gives the ageing report
40

How do you show actual versus target on a dashboard?

Dashboards and KPIsEasy
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Use a KPI card with actual, target and variance as both value and percentage, coloured by status. For trends, a bar or line for actual with a target line or band works well. Bullet charts compare actual against target and ranges compactly in tables. Make the period clear, such as month to date versus prorated monthly target, because comparing half a month of sales to a full month target looks falsely bad.

Frequently asked questions

What happens in the Data Analytics online assessment round?

Most companies start with an online test: aptitude and reasoning, a few MCQs on core Data Analytics concepts, and one or two short hands-on tasks. It filters a large number of applicants, so speed and accuracy both count.

How do I clear the online assessment round?

Practise under a timer: about one minute per MCQ and 15-20 minutes per hands-on task. Read every option before answering; tests often include two nearly correct choices. Use a stable internet connection, keep your camera on and avoid switching tabs in proctored tests.

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

Telephonic Interview questionsTechnical Round questionsManagerial Round questionsRapid-Fire 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