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Data Analytics Interview Questions for Telecom Companies

Q: Which telecom companies are covered?

This page links to 5 telecom companies, including Airtel, Jio Platforms, Ericsson, Nokia, Vodafone Idea. Each has its own page with rounds and a separate practice set.

Q: What rounds should I expect?

Common rounds at these companies include: HR round; Online assessment (aptitude + technical); Managerial round; Technical interview; Technical interview 1. The exact process varies by company and role.

40 questions weighted to the interview style of telecom companies — 11 easy, 28 medium, 1 hard.

How telecom companies interview for Data Analytics

Telecom and network companies handle huge volumes of customer and network data. Interviews focus on fundamentals, practical skills, and scenario questions about reliability, large datasets and customer-facing systems.

This set focuses on Python for Analysis (7), Statistics and Case Studies (6), SQL (5), Data Cleaning (4).

HR round Online assessment (aptitude + technical) Managerial round Technical interview Technical interview 1

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How do you make a dashboard accessible, for example for colour-blind users?

Dashboards and KPIsMedium

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Avoid red-green as the only difference; use colour-blind-safe palettes such as blue and orange, and add icons, labels or patterns so meaning never depends on colour alone. Ensure enough contrast between text and background, use readable font sizes, and add alt text to visuals. Keep a logical tab order for keyboard users, and offer a table view of chart data. Accessible dashboards are usually clearer for everyone.

Write Python code to extract valid 10-digit Indian mobile numbers from a messy text column.

Data CleaningMedium

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import re pattern = re.compile(r'(?:\+?91[\s-]?|0)?([6-9]\d{9})') def get_mobile(text): if not isinstance(text, str): return None digits = re.sub(r'[\s()-]', '', text) m = pattern.search(digits) return m.group(1) if m else None df['mobile'] = df['contact'].apply(get_mobile)

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

A/B Testing & ExperimentsEasy

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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.

What is statistical significance vs practical significance?

Statistics and Case StudiesMedium

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A result can be statistically significant yet too small to matter; always report effect size and business impact together with the p-value.

Your sales data contains negative quantities. What do you do with them?

Data CleaningMedium

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Do not delete them blindly. First find out what they mean: often they are returns, cancellations or credit notes, which are valid business events. Check with the source team or a documentation note. If they are returns, keep them but report gross sales, returns and net sales separately. If some are clearly entry errors, such as a negative quantity on a new order, flag and correct them and log the rule.

What does value_counts do in pandas, and how do you show percentages instead of counts?

Python for AnalysisEasy

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value_counts returns how many times each unique value appears in a Series, sorted from most to least frequent. It is a quick way to check category distribution or spot typos. Pass normalize=True to get proportions, then multiply by 100 for percentages, for example df['city'].value_counts(normalize=True).mul(100).round(1). Use dropna=False to include missing values, which are excluded by default and are often important.

Explain the types of SQL joins.

SQLEasy

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INNER returns matching rows, LEFT keeps all left rows, RIGHT keeps all right rows, FULL keeps all rows from both, and CROSS returns every combination.

What is the unit of randomisation in an experiment, and how do you choose it?

A/B Testing & ExperimentsMedium

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The unit of randomisation is what gets assigned to control or treatment: a user, a session, a device, a store or a city. Usually it should be the user, so one person always sees the same version. Session-level randomisation can show one user both versions and contaminate results. Your analysis unit should match it; if you randomise by user but analyse per page view, standard errors become too small.

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.

What is the difference between loc and iloc in pandas?

Python for AnalysisEasy

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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.

What are the main dimensions of data quality?

Data CleaningMedium

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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.

What are bookmarks and drill-through in Power BI?

Power BIMedium

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Bookmarks save a report view state for navigation; drill-through jumps to a detail page filtered to the item you picked.

Write Python code to calculate the sample size per group for a conversion test with a 4% baseline and a 10% relative lift.

A/B Testing & ExperimentsMedium

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from statsmodels.stats.power import NormalIndPower from statsmodels.stats.proportion import proportion_effectsize baseline = 0.04 target = baseline * 1.10 # 4.4% effect = proportion_effectsize(target, baseline) n = NormalIndPower().solve_power( effect_size=effect, alpha=0.05, power=0.8, alternative='two-sided' ) print(round(n)) # roughly 39,000 users per group

How do you let users choose Top N customers with a parameter in Tableau?

TableauMedium

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Create an integer parameter Top N with a range of 5 to 50 and show its control. Drag Customer to the Filters shelf, choose the Top tab, select By field, and set it to top Top N by SUM(Sales). Alternatively create a set based on the parameter so you can show Top N versus Others in one chart. If the Top N must respect a category filter, add that category filter to context.

How do you create a new column from conditions?

Python for AnalysisMedium

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Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.

Write pandas code to trim whitespace and lower-case every text column in a DataFrame.

Data CleaningEasy

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text_cols = df.select_dtypes(include='object').columns for col in text_cols: df[col] = ( df[col] .astype('string') .str.strip() .str.lower() .replace('', pd.NA) ) # also tidy column names df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')

When do you use a chi-square test?

Statistics and Case StudiesMedium

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A chi-square test of independence checks whether two categorical variables are related, using a contingency table of counts. Example: is preferred payment method related to city tier? It compares observed counts with the counts expected if there were no relationship. Use it with counts, not percentages, and make sure expected counts are not too small, roughly at least 5 per cell. A significant result shows association, not causation.

Estimate how many cups of tea are sold in a city of 1 crore people in a day.

Statistics and Case StudiesMedium

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State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.

Explain correlation vs regression.

Statistics and Case StudiesMedium

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

How do you calculate a rolling average?

SQLMedium

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df['sales'].rolling(window=7).mean() gives a 7-period moving average, useful to smooth daily noise.

How do you change data types and parse dates?

Python for AnalysisMedium

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df['date'] = pd.to_datetime(df['date']); df['amount'] = pd.to_numeric(df['amount'], errors='coerce'); astype('category') for repeated strings.

What is the difference between melt and pivot?

Python for AnalysisMedium

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melt converts wide data to long format; pivot converts long data to wide, and they are used to reshape data for analysis or charts.

What does the CALCULATE function do?

Power BIMedium

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It evaluates an expression after modifying the filter context, for example CALCULATE(SUM(Sales[Amount]), Region[Name] = "North").

What is the difference between ETL and ELT?

Data Modelling & WarehousingEasy

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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.

Write pandas code to find the top 2 products by revenue in each region.

Python for AnalysisMedium

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top2 = ( df.groupby(['region', 'product'], as_index=False)['revenue'] .sum() .sort_values(['region', 'revenue'], ascending=[True, False]) .groupby('region') .head(2) ) print(top2)

What is Power Query and why is it useful?

Power BIMedium

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An ETL tool inside Excel for importing, cleaning and shaping data with recorded steps that refresh automatically when the source changes.

Write SQL to find the top 3 products by revenue within each category.

SQLHard

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WITH ranked AS ( SELECT category, product, SUM(amount) AS revenue, DENSE_RANK() OVER ( PARTITION BY category ORDER BY SUM(amount) DESC ) AS rnk FROM sales GROUP BY category, product ) SELECT category, product, revenue FROM ranked WHERE rnk <= 3 ORDER BY category, rnk;

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

TableauMedium

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// 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

What are Type I and Type II errors?

Statistics and Case StudiesMedium

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A Type I error is a false positive: concluding there is an effect when there is none, for example launching a feature that does not actually lift sales. Its probability is alpha, often 0.05. A Type II error is a false negative: missing a real effect, with probability beta. Power is 1 minus beta. Larger samples reduce Type II errors, and the business cost of each mistake should guide which one you guard against more.

What is the difference between a fact table and a dimension table?

SQLEasy

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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.

How do you create a dual-axis chart in Tableau, for example sales bars with a profit margin line?

TableauEasy

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Drag Order Date to Columns as a continuous or discrete month, put Sales on Rows, then drag Profit Ratio to Rows next to it. Right-click the second pill and choose Dual Axis. On the Marks card set Sales to Bar and Profit Ratio to Line. Decide whether to synchronise axes: do not synchronise when units differ, like rupees and percentage. Label the axes clearly so readers know which is which.

Tell me about a time you found a mistake in a report after it had already been shared.

Data StorytellingMedium

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Situation: a day after sending a weekly report, I noticed one region's numbers included test orders. Task: correct it before decisions were made. Action: I quantified the impact, a 3 percent overstatement, and emailed the same audience with the corrected figures, a short explanation and the fix I had added to prevent repeats. Result: my manager appreciated the quick ownership, and the test-order filter became part of our shared dataset.

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.

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

Statistics and Case StudiesEasy

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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.

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.

A query on a 2-billion-row fact table in a cloud warehouse scans the whole table even when you filter one week. How do you fix it?

Data Modelling & WarehousingMedium

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Partition the table by date so the engine can prune partitions outside the filtered week, and cluster or sort by frequently filtered columns such as store or customer. Make sure the filter is on the partition column directly, not wrapped in a function like CAST or DATE_TRUNC that blocks pruning. Select only needed columns, because storage is columnar. For dashboards, also consider a pre-aggregated daily summary table.

What are parameters in Tableau and how do you use them?

TableauMedium

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A parameter is a single user-controlled value, such as a number, date or string, that is independent of the data. On its own it does nothing; you reference it in calculations, filters, reference lines or bins. Common uses are a Top N selector, a what-if growth rate, or a measure switcher where a CASE statement picks Sales, Profit or Quantity based on the parameter. Parameter actions let users set it by clicking a mark.

Tell me about a time a query you wrote produced wrong results.

SQLMedium

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Situation: my weekly active users number jumped 30 percent overnight. Task: find out whether it was real before the report went out. Action: I traced the query and found a new join to a device table created duplicate users. I changed the count to COUNT(DISTINCT user_id), added a row-count check, and informed my manager before publishing. Result: the corrected number went out on time, and I added that check to every report.

Write pandas code for a pivot table of sales by region and category with sum and count.

Python for AnalysisMedium

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import pandas as pd summary = pd.pivot_table( df, index='region', columns='category', values='sales', aggfunc=['sum', 'count'], fill_value=0, margins=True # adds an All row and column ) print(summary)

What are vanity metrics?

Dashboards and KPIsEasy

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Vanity metrics look impressive but do not help decisions or reflect real value, such as total app downloads, total registered users or social media likes. They usually only go up. Better alternatives are actionable metrics such as active users, retention by cohort, conversion rate and revenue per user. A quick test: if the number changed, would anyone do anything differently? If not, it probably does not belong on the main dashboard.