Skip to content
Data Analytics
All courses OffersPlacement Report
‹ Back to previous sectionCourse homeAll page URLs

Data Analytics Telephonic Interview Questions & Answers

30 questions for the telephonic interview stage — 14 easy, 16 medium.

About the telephonic interview round

The telephonic or screening round is a 15-30 minute call with a recruiter or engineer. It checks your basics, your CV and whether you can explain Data Analytics concepts clearly without a screen to point at.

Tips for the telephonic interview round

  • Keep your CV in front of you and be ready to explain every line on it.
  • Answer in short, clear sentences; there is no whiteboard, so structure matters.
  • Take the call somewhere quiet and confirm the time in advance.
  • Prepare one question to ask the interviewer at the end.
Solved — telephonic interviewStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
3 questions free Unlock the rest in seconds
01

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

Data StorytellingEasy
Save to ▾
Show answer
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.
02

Write SQL to find the first-touch and last-touch channel for every user who converted.

Web & Marketing AnalyticsMedium
Save to ▾
Show answer
WITH touches AS ( SELECT t.user_id, t.channel, t.touch_ts, ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts) AS first_rank, ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts DESC) AS last_rank FROM marketing_touches t JOIN conversions c ON c.user_id = t.user_id AND t.touch_ts <= c.converted_ts ) SELECT user_id, MAX(CASE WHEN first_rank = 1 THEN channel END) AS first_touch, MAX(CASE WHEN last_rank = 1 THEN channel END) AS last_touch FROM touches GROUP BY user_id;
03

Import mode vs DirectQuery?

Power BIMedium
Save to ▾
Show answer
Import loads data into memory for fast visuals; DirectQuery queries the source live, giving fresh data but slower performance and some DAX limits.
04

Explain Matplotlib vs Seaborn.

Python for AnalysisMedium
Save to ▾
Show answer
Matplotlib is the low-level plotting library with full control; Seaborn builds on it with attractive statistical charts and simpler syntax.
05

Your FILTER formula shows #SPILL! instead of results. Why, and how do you fix it?

ExcelEasy
Save to ▾
Show answer
Dynamic array formulas such as FILTER, UNIQUE and SORT return several values that spill into neighbouring cells. #SPILL! appears when that spill range is blocked by existing data, merged cells, or when the formula sits inside an Excel Table, which does not allow spilling. Fix it by clearing the blocking cells, unmerging, or moving the formula outside the Table. Select the error cell to see the dashed border of the range it needs.
06

You share a forecasting workbook and users keep overwriting formulas. How do you protect it?

ExcelMedium
Save to ▾
Show answer
Separate input cells from calculation cells and colour inputs clearly. Unlock only the input cells through Format Cells, Protection, then protect the sheet so formulas cannot be edited, and optionally hide formulas. Add data validation on inputs to block wrong values such as negative quantities. Protect workbook structure so sheets cannot be deleted, keep a version history, and add a short instructions sheet so users know where to type.
07

What is data cleaning and why does it take so much of an analyst's time?

Data CleaningEasy
Save to ▾
Show answer
Data cleaning means fixing or removing incorrect, incomplete, duplicated or inconsistent data so analysis is trustworthy. Real data comes from many systems and manual entry, so you meet typos, mixed formats, missing values and duplicates. Analysts often spend half or more of a project on it because every later chart and model depends on it. Wrong inputs give confident but wrong answers, which is worse than no answer.
08

What is a story in Tableau?

TableauEasy
Save to ▾
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.
09

Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?

TableauMedium
Save to ▾
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.
10

Which Python libraries do analysts use most?

Python for AnalysisEasy
Save to ▾
Show answer
Pandas for data handling, NumPy for numerics, Matplotlib and Seaborn for charts, and openpyxl or SQLAlchemy for files and databases.
11

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

Data StorytellingMedium
Save to ▾
Show answer
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.
12

What is a role-playing dimension?

Data Modelling & WarehousingMedium
Save to ▾
Show answer
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.
13

What types of dashboard actions does Tableau offer?

TableauMedium
Save to ▾
Show answer
Filter actions filter other sheets when a user selects or hovers on a mark, such as clicking a region to filter a product table. Highlight actions emphasise related marks without removing others. URL actions open a web page with field values in the link. Go to Sheet navigates to another dashboard. Parameter and set actions update a parameter or set based on selection, enabling advanced interactions like dynamic drill-downs and comparisons.
14

When would you drop missing values, and when would you impute with mean or median?

Data CleaningMedium
Save to ▾
Show answer
Drop rows when few are missing, roughly under 5 percent, and missingness looks random. Impute when dropping would lose too much data. Use the median for skewed numbers such as income or order value, the mean for roughly symmetric data, and the mode or an Unknown category for categoricals. Group-wise imputation, such as median by city, is better than one global value. Always add a flag column showing what was imputed.
15

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

Python for AnalysisEasy
Save to ▾
Show answer
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.
16

Write a Tableau table calculation for year-over-year sales growth.

TableauMedium
Save to ▾
Show answer
// with YEAR(Order Date) in the view, Compute Using: Table (across) (ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN(SUM([Sales])), -1)) // the built-in Quick Table Calculation 'Year over Year Growth' generates similar logic // format the field as a percentage
17

What are the main dimensions of data quality?

Data CleaningMedium
Save to ▾
Show answer
Common dimensions are accuracy, values reflect reality; completeness, required fields are present; consistency, the same fact matches across systems; validity, values follow formats and rules such as a 6-digit pin code; uniqueness, no unwanted duplicates; and timeliness, data is fresh enough for its use. Measuring these with simple checks, like percent missing or duplicate rate, turns vague complaints about bad data into fixable issues.
18

What is the difference between an outlier and a data error?

Data CleaningMedium
Save to ▾
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.
19

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

Statistics and Case StudiesEasy
Save to ▾
Show answer
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.
20

What is the medallion architecture (bronze, silver, gold)?

Data Modelling & WarehousingMedium
Save to ▾
Show answer
Medallion architecture organises a lakehouse into three layers. Bronze holds raw data exactly as ingested, for audit and reprocessing. Silver holds cleaned, deduplicated, typed and joined data. Gold holds business-ready tables such as star schemas and aggregates used by dashboards. Each layer is built from the previous one by repeatable jobs, so if a cleaning rule changes you can rebuild silver and gold from bronze.
21

What does Flash Fill do in Excel, and when should you not rely on it?

ExcelEasy
Save to ▾
Show answer
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.
22

A junior analyst wants to load one huge flat table with every column into Power BI instead of a star schema. How do you advise them?

Data Modelling & WarehousingMedium
Save to ▾
Show answer
A flat table is quick to start and fine for a small one-off analysis. But at scale it bloats the model because repeated text attributes compress poorly, makes DAX filters and time intelligence harder, and breaks when you add a second fact like targets or returns. I would show them a star schema with one fact and a few dimensions, compare model size and measure speed, and let the results convince them.
23

Tell me about a time you were at risk of missing a reporting deadline.

Dashboards and KPIsEasy
Save to ▾
Show answer
Situation: a month-end report was due at 10 am, and at 8 am the source data had not loaded. Task: deliver something reliable. Action: I informed my manager immediately, checked with the data team for an estimate, and prepared the report on the previous day's data, clearly labelled. Result: leadership got the report on time with a caveat, and I sent the final version by noon. We later added a data arrival alert.
24

What is the difference between DELETE, TRUNCATE and DROP?

SQLEasy
Save to ▾
Show answer
DELETE removes chosen rows and can be rolled back; TRUNCATE empties the table quickly; DROP removes the table structure itself.
25

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

ExcelEasy
Save to ▾
Show answer
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.
26

Tell me about a time a dashboard you built changed a business decision.

Dashboards and KPIsMedium
Save to ▾
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.
27

What are variables (VAR) in DAX and why are they useful?

Power BIMedium
Save to ▾
Show answer
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.
28

How should you lay out a dashboard page?

Dashboards and KPIsEasy
Save to ▾
Show answer
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.
29

What are novelty and primacy effects in experiments?

A/B Testing & ExperimentsEasy
Save to ▾
Show answer
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.
30

Tell me about a time you had to learn a new tool or library quickly for a project.

Python for AnalysisEasy
Save to ▾
Show answer
Situation: in my internship the team needed survey results analysed in Python, which I had only used in coursework. Task: deliver cleaned data and charts within a week. Action: I spent two evenings on pandas and Seaborn tutorials using our real file, kept a notes document of commands, and asked a senior to review my first notebook. Result: I delivered on day five, and my notebook became the template for the next survey.

Frequently asked questions

What happens in the Data Analytics telephonic interview round?

The telephonic or screening round is a 15-30 minute call with a recruiter or engineer. It checks your basics, your CV and whether you can explain Data Analytics concepts clearly without a screen to point at.

How do I clear the telephonic interview round?

Keep your CV in front of you and be ready to explain every line on it. Answer in short, clear sentences; there is no whiteboard, so structure matters. Take the call somewhere quiet and confirm the time in advance.

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 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