Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About UST hiring for Data Analytics roles
UST is a digital transformation and IT services company with large delivery centres in India, particularly in Kerala. It provides software development, testing, cloud, data and AI services for clients in healthcare, retail, banking and semiconductors. It hires in India for software engineers, test engineers, data engineers and analysts.
For Data Analytics roles, this UST practice set focuses most on Data Modelling & Warehousing (5), Statistics and Case Studies (5), Tableau (4), Power BI (4). It has 20 easy, 18 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
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)
02
How do you split a full name column into first and last name in Excel?
-- A2 = 'Priya Ramesh Iyer'
-- first name
=TEXTBEFORE(TRIM(A2), " ")
-- last name (text after the last space)
=TEXTAFTER(TRIM(A2), " ", -1)
-- older Excel: first name
=LEFT(TRIM(A2), FIND(" ", TRIM(A2)&" ")-1)
-- or use Data > Text to Columns or Flash Fill (Ctrl+E) for a one-off task
03
Many reporting queries in your team use SELECT DISTINCT to remove duplicate rows. What is the risk?
SELECT DISTINCT often hides a real problem, such as a join on the wrong key or a one-to-many join that multiplies rows. The output looks clean, but sums computed before the DISTINCT, or legitimate repeated rows, can be wrong, and DISTINCT adds a costly sort on large tables. Instead, find why duplicates appear, fix the join or aggregate the child table first, and add a uniqueness test on the key.
04
What is the difference between a view and a materialised view?
# Fill missing delivery_days with the median for that city
df['delivery_days'] = (
df['delivery_days']
.fillna(df.groupby('city')['delivery_days'].transform('median'))
)
# Rows in a city with no values at all stay NaN; fall back to overall median
df['delivery_days'] = df['delivery_days'].fillna(df['delivery_days'].median())
06
What is the difference between dimensions and measures in Tableau?
Dimensions are qualitative fields such as region, product or date that slice data into groups and create headers. Measures are quantitative fields such as sales or profit that get aggregated, by default with SUM. Dragging a dimension to rows creates one row per member; dragging a measure creates an axis. A numeric field like customer ID should be converted to a dimension so it is not summed.
07
Write SQL to calculate daily active users and the DAU/MAU stickiness ratio.
WITH dau AS (
SELECT CAST(event_ts AS DATE) AS day,
COUNT(DISTINCT user_id) AS dau
FROM events
GROUP BY CAST(event_ts AS DATE)
)
SELECT d.day,
d.dau,
(SELECT COUNT(DISTINCT e.user_id)
FROM events e
WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day) AS mau,
ROUND(100.0 * d.dau / (SELECT COUNT(DISTINCT e.user_id)
FROM events e
WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day), 1) AS stickiness_pct
FROM dau d
ORDER BY d.day;
08
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.
09
What is regression to the mean? Give a business example.
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.
Data storytelling is communicating insights by combining three things: data, the evidence; visuals, charts that make patterns obvious; and narrative, the context and so-what that explains why it matters and what to do. A table of numbers informs, but a story persuades someone to act. For an analyst, it means leading with the key finding, showing just enough evidence, and ending with a clear recommendation.
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.
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.
Power BI Desktop is the free Windows application where you connect data, model it, write DAX and design reports. Power BI Service is the cloud platform where reports are published, shared, scheduled for refresh, and organised into workspaces and apps. Power BI Report Server is an on-premises option for organisations that cannot use the cloud, with fewer features. Most analysts build in Desktop and share through the Service.
16
Write SQL to generate a simple date dimension for 2026.
-- PostgreSQL syntax
CREATE TABLE dim_date AS
SELECT
TO_CHAR(d, 'YYYYMMDD')::INT AS date_key,
d::DATE AS full_date,
EXTRACT(YEAR FROM d) AS year,
EXTRACT(QUARTER FROM d) AS quarter,
EXTRACT(MONTH FROM d) AS month_no,
TO_CHAR(d, 'Mon') AS month_name,
TO_CHAR(d, 'Dy') AS weekday,
EXTRACT(ISODOW FROM d) IN (6, 7) AS is_weekend
FROM GENERATE_SERIES(DATE '2026-01-01', DATE '2026-12-31', INTERVAL '1 day') AS d;
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.
18
What is the unit of randomisation in an experiment, and how do you choose it?
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.
19
Design a dashboard for CXOs — what principles do you follow?
Start from the business questions, show 4–6 top KPIs with trend vs target, allow drill-downs, keep colours consistent, remove clutter, add time/region filters and give one clear insight per visual.
The normal distribution is a symmetric, bell-shaped distribution described by its mean and standard deviation. The empirical rule says about 68% of values fall within one standard deviation of the mean, 95% within two and 99.7% within three. For example, if delivery times average 30 minutes with an SD of 5, about 95% arrive in 20 to 40 minutes. Many business metrics are skewed, so check before assuming normality.
22
What are novelty and primacy effects in experiments?
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.
23
What is the difference between sets and groups in Tableau?
A group combines dimension members into higher-level categories, such as merging small states into Others; it is static and each member belongs to one group. A set splits members into In and Out based on a selection or condition, such as top 10 customers by sales, and can be dynamic and recalculate with data. Sets can be combined, used in calculations and changed by set actions, making them more powerful for interactive analysis.
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.
25
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.
26
Write Excel formulas to get a sorted list of unique customer names and their count.
-- 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)
// 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])
Dimensional modelling is a way of designing analytical tables around business processes. Numeric events such as sales or payments go into fact tables, and descriptive context such as customer, product, store and date goes into dimension tables. The design is easy for business users to understand and fast for BI tools to aggregate. For example, a retail model might have one sales fact joined to date, product, store and customer dimensions.
30
How do you present insights to non-technical stakeholders?
Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.
31
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
32
What is the difference between population and sample?
SUM adds up a single column in the current filter context. SUMX is an iterator: it goes row by row over a table, evaluates an expression for each row, then sums the results. Use SUMX when the value must be computed per row first, for example SUMX(Sales, Sales[Qty] * Sales[Price]). Iterators are flexible but can be slower on very large tables, so storing a simple column is sometimes better.
35
What is the difference between a fact table and a dimension table?
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.
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.
A tool that summarises large tables by dragging fields into rows, columns, values and filters, so you can slice sales by region or month without writing formulas.
Variance is the average squared distance of values from their mean; standard deviation is its square root, in the same units as the data. They measure spread. Two stores may both average 50,000 rupees daily, but one with a standard deviation of 2,000 is stable while one with 20,000 is volatile. For samples, divide by n minus 1 instead of n to get an unbiased estimate.
Visually with a box plot, with the IQR rule (below Q1 − 1.5×IQR or above Q3 + 1.5×IQR) or a z-score above 3. Then decide with the business whether to cap, remove or keep them.
40
Organic search traffic fell 40% overnight. How do you investigate?
First check whether it is a tracking problem: did the analytics tag break on some templates, did consent settings change, does server log or backend order data show the same fall? If the drop is real, check the search console for impressions and rankings, recent site releases such as changed URLs, missing redirects, noindex tags or robots.txt changes, and whether a search algorithm update or seasonal pattern matches. Then break the drop down by page group.
Tips to crack the UST Data Analytics interview
Practise aptitude and coding questions for the assessment, focusing on accuracy within time limits.
Revise SQL, testing concepts and your main programming language, and prepare examples from your projects.
Learn about UST's history as UST Global and its large Thiruvananthapuram campus for company questions.
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; UST interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the UST Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Coding test; Technical interview; 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 UST Data Analytics interview?
This practice set leans on Data Modelling & Warehousing (5), Statistics and Case Studies (5), Tableau (4), Power BI (4). Revise those first, then practise explaining one or two of your own projects end to end, because most UST interviewers ask you to walk through real work.
Are these the exact questions asked at UST?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like UST's. They are not a leaked or verified list of past questions.
Where does UST hire Data Analytics professionals in India?
UST has large teams in Thiruvananthapuram, Kochi, Bengaluru, Chennai, Hyderabad. Openings and locations change often, so check the UST careers page and filter by city and experience.