Data Analytics Interview Questions in Thiruvananthapuram
36 practice questions with answers for Data Analytics interviews in Thiruvananthapuram.
Data Analytics jobs in Thiruvananthapuram
Thiruvananthapuram's Technopark is one of India's oldest IT parks, hosting IT-services, product and engineering-services companies. Data and testing teams here often work with global clients in finance, travel and healthcare.
Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Thiruvananthapuram.
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01
Site conversion rate rose from 2.0% to 2.4%, but total revenue stayed flat. What would you check?
Check the components: sessions may have fallen, for example after cutting paid campaigns, which removes low-intent traffic and lifts the rate without new sales. Average order value may have dropped because of discounts or a mix shift towards cheaper products. Also check whether conversion definitions or tracking changed. Revenue equals sessions times conversion rate times AOV, so break down the change across those three factors by channel.
02
Difference between a CTE, a subquery and a temp table?
A subquery is inline; a CTE is a named, readable block for one statement; a temp table is physically stored for the session and can be indexed and reused.
03
What are additive, semi-additive and non-additive facts?
Additive facts, like sales amount or quantity, can be summed across every dimension. Semi-additive facts, like account balance or stock on hand, can be summed across accounts or stores but not across time; for time you take the closing or average value. Non-additive facts, like percentages, ratios or unit prices, cannot be summed at all, so you store their components (profit and revenue) and calculate the ratio at query time.
04
What is CUPED and how does it speed up experiments?
CUPED is a variance reduction method that uses each user's pre-experiment behaviour, such as spend in the previous four weeks, as a covariate. You adjust the outcome by removing the part explained by past behaviour, which is unrelated to the treatment because assignment was random. The lower variance gives tighter confidence intervals, so you can detect the same effect with fewer users or a shorter test, often 20-50% faster.
05
Write a Tableau calculated field that keeps only sales from the last 30 days.
// Calculated field: Last 30 Days Flag
DATEDIFF('day', [Order Date], TODAY()) <= 29
AND [Order Date] <= TODAY()
// Put this field on Filters and keep True.
// For a sales measure instead of a filter:
// IF DATEDIFF('day', [Order Date], TODAY()) <= 29 THEN [Sales] END
06
Write a DAX measure for year-over-year sales growth percentage.
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
Excel guessed the column was a number and dropped leading zeros; the same happens with long IDs turning into scientific notation. Identifiers that are not used in arithmetic should be stored as text. Import through Data, From Text/CSV or Power Query and set the column type to Text, or read it in pandas with dtype={'pincode': str}. To repair, use =TEXT(A2, "000000") when the original length is known.
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
A correlated subquery refers to a column from the outer query, so it is logically evaluated once per outer row. Example: find employees earning above their department average with WHERE salary > (SELECT AVG(salary) FROM emp e2 WHERE e2.dept_id = e1.dept_id). It is readable but can be slow on large tables. The same result is often faster with a window function such as AVG(salary) OVER (PARTITION BY dept_id).
11
Why would you publish a Tableau data source separately instead of embedding it in each workbook?
A published data source lives on Tableau Server or Cloud and can be reused by many workbooks. Calculations, joins, field names and extract refresh schedules are defined once, so every report uses the same revenue logic and the database is queried once per refresh instead of once per workbook. You can certify it and set permissions. Embedded sources are fine for a quick personal analysis but drift over time.
12
What is the difference between a clustered and a non-clustered index?
A degenerate dimension is a dimension key stored directly in the fact table with no separate dimension table, because it has no other attributes worth storing. Invoice number, order number and transaction ID are typical examples. They are useful for grouping order lines back into an order, counting distinct orders or tracing a row to the source system. Creating a separate table just for the number would add a useless join.
16
What are relationships and cardinality in Power BI?
Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.
17
Describe a time you explained the limits of attribution data to a senior stakeholder.
Situation: a director wanted to cut social ads because last-click reports showed almost no sales. Task: give a fairer view. Action: I showed with simple path examples that social often started journeys that ended in search, compared first-click and data-driven views, and proposed a two-city holdout test. Result: the test showed meaningful incremental sales, so the budget was trimmed modestly instead of cut, and attribution caveats were added to the dashboard.
18
What is a date table in Power BI and why should you mark it as one?
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.
// 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
21
Write SQL to calculate sessions, orders and conversion rate by traffic source.
SELECT
s.traffic_source,
COUNT(*) AS sessions,
COUNT(o.order_id) AS orders,
ROUND(100.0 * COUNT(o.order_id) / COUNT(*), 2) AS conversion_pct
FROM sessions s
LEFT JOIN orders o
ON o.session_id = s.session_id
GROUP BY s.traffic_source
ORDER BY sessions DESC;
-- assumes at most one order per session; otherwise use COUNT(DISTINCT o.session_id)
22
What does apply do in pandas and why can it be slow?
apply runs a Python function on each row or column of a DataFrame, or each value of a Series. It is flexible but slow on large data because it loops in Python rather than using optimised C code. For example, df['total'] = df['qty'] * df['price'] is far faster than an apply with a lambda. Prefer vectorised operations, np.where, np.select, map with a dictionary, or string accessor methods first.
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;
25
What is the grain of a fact table and why should you decide it first?
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.
26
Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.
First confirm it is real: check tracking events, data delays and whether the denominator changed. Then segment by app version, platform, device, payment method and traffic source. If the drop is concentrated in the new version or one payment option, it points to a bug or UX change. Look at funnel steps to find where users drop. Compare with seasonality and campaigns, then share findings with product and engineering quickly.
ROW_NUMBER gives a unique sequence. RANK gives ties the same rank and skips the next (1, 1, 3). DENSE_RANK gives ties the same rank without gaps (1, 1, 2).
29
Where should data cleaning happen: Excel, SQL, Python or Power Query?
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.
30
How do you make sure a metric like active customer means the same thing across all teams?
Write a clear definition in a shared metric dictionary: formula, filters, time window, grain, owner and source table, for example a customer with at least one paid order in the last 90 days. Implement it once, in a governed dataset or semantic layer, and have all dashboards reuse it rather than recalculating. Version changes, announce them and show the definition in the dashboard tooltip. Review new metrics before they are published.
Broadcasting lets NumPy perform operations on arrays of different shapes without copying data, by virtually stretching the smaller array. For example, subtracting a 1D array of column means from a 2D array centres every column in one line: X - X.mean(axis=0). Shapes are compatible when trailing dimensions are equal or one of them is 1. It is a key reason vectorised code is much faster than Python loops.
32
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?
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.
33
What is the difference between an A/B test, an A/B/n test and a multivariate test?
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.
34
Write Python code to calculate the sample size per group for a conversion test with a 4% baseline and a 10% relative lift.
Situation: after a checkout redesign, purchases in our analytics fell 35% overnight while orders were normal. Task: find the cause and protect the weekly report. Action: I compared analytics with the order table by page and device, found the purchase tag no longer fired on the new UPI payment success page, and worked with developers to fix it. Result: I backfilled the gap from order data, added a footnote and set up a daily mismatch alert.
A factless fact table records that an event or relationship happened but has no numeric measures, only dimension keys. Examples are student attendance (student, class, date) or which products were on promotion in which store each day. You analyse it by counting rows. A promotion coverage table is useful for questions like which promoted products did not sell, by comparing it with the sales fact.
Which companies hire Data Analytics professionals in Thiruvananthapuram?
Companies with large offices in Thiruvananthapuram include UST, Tata Elxsi, Quest Global. Use their company pages below to practise employer-specific sets.
Are Data Analytics interviews in Thiruvananthapuram different from other cities?
The questions depend more on the company than the city. Thiruvananthapuram's mix of employers decides the style: Data and testing teams here often work with global clients in finance, travel and healthcare
How do I find Data Analytics jobs in Thiruvananthapuram?
Shortlist companies with Thiruvananthapuram offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Thiruvananthapuram filter, and practise the questions on this page before each interview.