Samsung Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Samsung hiring for Data Analytics roles
Samsung Electronics makes smartphones, consumer electronics, semiconductors and home appliances. Its India R&D institutes in Bengaluru, Noida and Delhi work on mobile software, AI, camera, connectivity and data. It hires for software engineers, test engineers and data professionals, with campus hiring known for coding-focused tests.
For Data Analytics roles, this Samsung practice set focuses most on Dashboards and KPIs (8), Excel (6), Data Cleaning (4), Power BI (4). It has 2 easy, 33 medium, 5 hard questions, so you can start easy and work up to the harder ones.
Company type
Hardware & semiconductors
Headquarters
Suwon, South Korea
Main India offices
Bengaluru, Noida, Delhi
Samsung Data Analytics interview process
Online coding test (Samsung competency test)
Technical interview 1
Technical interview 2
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for SamsungStart practicing
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01
How do you write a good hypothesis for an experiment?
A good hypothesis names the change, the expected effect, the metric and the reason. For example: showing delivery date on the product page will increase add-to-cart rate by at least 2% because uncertainty about delivery stops purchases. It should be written before the test starts, be measurable, and state the null hypothesis of no difference. A vague hypothesis like 'the new page will be better' leads to cherry-picking metrics afterwards.
02
How do you remove duplicates in Excel but keep the latest record?
Sort by date (newest first), then Data → Remove Duplicates on the key column — Excel keeps the first occurrence. Or use UNIQUE / a COUNTIFS helper column.
03
Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).
-- 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
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.
05
You launched a dashboard a month ago and usage logs show almost nobody opens it. What do you do?
Talk to the intended users before redesigning. Find out whether they know it exists, whether it answers their real questions, whether they trust the numbers, and whether it fits their routine. Common fixes are fewer, sharper KPIs, faster loading, a scheduled email snapshot, or embedding it in a meeting that already happens. Then measure usage again. Sometimes the honest answer is that the dashboard is not needed and can be retired.
06
Estimate how many cups of tea are sold in a city of 1 crore people in a day.
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.
07
Write pandas code to standardise inconsistent city names such as 'delhi ', 'New Delhi' and 'DELHI'.
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.
10
Average delivery time improved by 4 minutes, but customer complaints about late deliveries went up. How do you investigate?
An average can improve while the tail gets worse. I would look at the distribution: median, 90th and 95th percentiles and the share of orders beyond the promised time. Then break it down by city, time slot and restaurant to see where late orders cluster. Also check whether promised times changed, whether complaint tracking changed and whether a few very fast orders pulled the mean down.
11
What steps would you apply in Power Query to clean a messy system export?
Typical steps: remove top junk rows and promote the real header row, remove blank rows and columns, set correct data types, trim and clean text, split combined columns such as Name - City, replace error values, fill down for grouped labels, remove duplicates on the business key, and unpivot wide month columns. Rename steps meaningfully so others can follow them, and keep the query foldable when the source is a database.
12
Write a DAX measure for a KPI card showing achievement against target and a colour for conditional formatting.
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
SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
14
Explain FIXED, INCLUDE and EXCLUDE LOD expressions in Tableau.
Level of Detail expressions compute aggregates at a level different from the view. FIXED computes at the dimensions you specify regardless of the view, for example { FIXED [Customer] : SUM([Sales]) } for lifetime sales per customer. INCLUDE adds a dimension to the view's level, such as average of per-customer sales by region. EXCLUDE removes a dimension, useful for comparing each month to the yearly total. FIXED ignores normal dimension filters unless they are context filters.
15
On day 4 of a planned 14-day test, the product manager wants to stop because the dashboard shows treatment winning. What do you say?
I would explain that early results swing a lot and stopping on a good day inflates false positives, and that four days miss weekly patterns like weekend shopping. I would show how the lift has moved day by day and agree to the planned end date. If speed really matters, we can agree a sequential testing rule for future tests, but changing the rule mid-test after seeing data is not valid.
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
17
How do you make a dashboard accessible, for example for colour-blind users?
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.
18
Your sales data contains negative quantities. What do you do with them?
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.
Core KPIs: headcount, monthly and annualised attrition rate, voluntary versus involuntary split, and early attrition within the first 90 days or first year. Breakdowns by department, location, manager, tenure band, performance rating and salary band show where it concentrates. Add hiring metrics such as time to fill and offer acceptance rate, and exit reasons from exit interviews. Protect privacy by hiding very small groups.
21
Tell me about a time tracking broke and you had to fix the reporting.
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.
22
Tell me about a time you worked with a marketing team to improve a campaign using data.
Situation: a lead generation campaign had a high cost per lead and sales said leads were poor. Task: find what to change. Action: I joined ad data with CRM outcomes and found two audience segments produced most leads but almost no closed deals, while one smaller segment converted well. Result: we moved budget to that segment and changed the form questions; cost per qualified lead fell about 30% in six weeks.
23
Your NOT IN query unexpectedly returns zero rows. Why might that happen, and how do EXISTS and IN differ?
IN checks whether a value is in a list or subquery result; EXISTS checks whether a subquery returns at least one row and stops at the first match. Results are the same in most cases, and modern optimisers often run them similarly. The big difference is NOT IN with NULLs: if the subquery returns any NULL, NOT IN returns no rows. NOT EXISTS behaves correctly, so prefer it for anti-joins.
SELECT region,
SUM(CASE WHEN quarter = 'Q1' THEN amount ELSE 0 END) AS q1,
SUM(CASE WHEN quarter = 'Q2' THEN amount ELSE 0 END) AS q2,
SUM(CASE WHEN quarter = 'Q3' THEN amount ELSE 0 END) AS q3,
SUM(CASE WHEN quarter = 'Q4' THEN amount ELSE 0 END) AS q4
FROM sales
GROUP BY region;
-- conditional aggregation works in every database; some also offer PIVOT
26
Write a Tableau calculation to find each customer's first purchase date and flag new versus repeat orders.
// First Purchase Date
{ FIXED [Customer ID] : MIN([Order Date]) }
// Order Type
IF [Order Date] = [First Purchase Date]
THEN 'New'
ELSE 'Repeat'
END
// Cohort Month for retention analysis
DATETRUNC('month', [First Purchase Date])
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
28
Write SQL to calculate monthly customer churn rate.
WITH active AS (
SELECT DISTINCT customer_id,
DATE_TRUNC('month', order_date) AS month
FROM orders
)
SELECT a.month,
COUNT(*) AS active_customers,
COUNT(*) - COUNT(n.customer_id) AS churned_next_month,
ROUND(100.0 * (COUNT(*) - COUNT(n.customer_id)) / COUNT(*), 1) AS churn_pct
FROM active a
LEFT JOIN active n
ON n.customer_id = a.customer_id
AND n.month = a.month + INTERVAL '1 month'
GROUP BY a.month
ORDER BY a.month;
29
Tell me about a time you migrated or rebuilt reports in a new BI tool.
Situation: our team moved 15 Excel and older BI reports into Tableau. Task: I owned the sales set. Action: I listed every metric with its definition, rebuilt the shared data source first, then the dashboards, and ran side-by-side number checks for two weeks with the old reports. I held short demos for users. Result: all reports went live with matching numbers, and refresh effort fell from a day to an automatic schedule.
30
How do you measure impact when a proper A/B test is not possible?
Use quasi-experimental methods. Difference-in-differences compares the change in a treated group, such as cities that got a new policy, with the change in similar untreated cities over the same period. Synthetic control builds a weighted mix of untreated regions to mimic the treated one. Regression discontinuity uses a cut-off, like a credit score threshold. Always check pre-period trends and state the assumptions clearly.
MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.
It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.
33
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.
34
Your pandas script takes 40 minutes on 10 million rows. How do you speed it up?
First profile to find the slow step instead of guessing. Common fixes: replace row-wise apply and loops with vectorised operations, convert repeated strings to category dtype, downcast numeric types, read only needed columns with usecols, and filter early. Push heavy joins and aggregations into SQL. If it is still slow, use a columnar engine such as DuckDB or Polars, or store intermediate data as Parquet instead of CSV.
35
What is referential integrity and how do orphan records appear in analytics data?
Referential integrity means every foreign key value points to an existing row in the parent table, for example every order's customer_id exists in customers. Orphan records break this rule. They appear when source systems load tables at different times, records are deleted without cascading, or IDs are formatted differently across systems. Detect them with a LEFT JOIN where the parent key is NULL, and decide whether to fix, map or flag them.
36
What is the difference between a t-test and a z-test?
Both compare means. A z-test assumes the population standard deviation is known or the sample is large, so the normal distribution applies. A t-test estimates standard deviation from the sample and uses the t-distribution, which has heavier tails for small samples. In practice analysts mostly use t-tests: one-sample, independent two-sample (Welch's version if variances differ) or paired, for before-after measurements on the same users.
37
What are variables (VAR) in DAX and why are they useful?
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.
38
What is an action title and why should you use one?
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.
39
A manager asks for a dashboard that refreshes every minute. How do you decide the right refresh frequency?
Match refresh to how quickly decisions are made. A monthly board pack needs daily refresh at most, while a warehouse dispatch board may need 15-minute or live updates. Faster refresh costs more in source load, licensing and complexity, and can show incomplete data. Ask how quickly someone will act on a change, check when upstream systems finish loading, and always display the last refreshed time on the dashboard.
40
How do you merge two DataFrames on different column names?
pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.
Tips to crack the Samsung Data Analytics interview
Practise Samsung-style coding problems based on BFS, DFS, backtracking and simulation without using built-in libraries.
Revise C, C++ or Java fundamentals deeply along with operating systems and data structures.
Read about Samsung R&D work in India on Galaxy devices, One UI and on-device AI.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Samsung interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Samsung Data Analytics interview?
Candidates usually go through 4 stages: Online coding test (Samsung competency test); Technical interview 1; Technical interview 2; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Samsung Data Analytics interview?
This practice set leans on Dashboards and KPIs (8), Excel (6), Data Cleaning (4), Power BI (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Samsung interviewers ask you to walk through real work.
Are these the exact questions asked at Samsung?
No. These are curated practice questions that match the skills and difficulty usually tested in hardware & semiconductors interviews like Samsung's. They are not a leaked or verified list of past questions.
Where does Samsung hire Data Analytics professionals in India?
Samsung has large teams in Bengaluru, Noida, Delhi. Openings and locations change often, so check the Samsung careers page and filter by city and experience.