01
What is vectorisation and why does it matter?
Show answer
Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.
Persistent Systems Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Persistent Systems hiring for Data Analytics roles
Persistent Systems is an Indian technology services company focused on software product engineering, digital transformation, cloud and data services. It serves clients in banking, healthcare, life sciences and software. In India it hires for software engineers, quality engineers, data engineers and analysts, often through campus drives with coding-focused assessments.
For Data Analytics roles, this Persistent Systems practice set focuses most on Excel (6), Statistics and Case Studies (6), Data Modelling & Warehousing (5), Tableau (5). It has 13 easy, 25 medium, 2 hard questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Pune, India
- Main India offices
- Pune, Nagpur, Bengaluru, Hyderabad, Goa
Persistent Systems Data Analytics interview process
- Online assessment (aptitude + coding)
- Technical round 1
- Technical round 2
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
What are absolute and relative cell references?
Show answer
Relative references change when copied (A1); absolute references stay fixed ($A$1); mixed references lock only the row or column (A$1 or $A1).
03
What is a pivot table and when do you use it?
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.
04
What is an A/A test and why would you run one?
Show answer
An A/A test splits users into two groups that both see the same version. Since nothing differs, you expect no significant difference except about 5% of the time by chance. It checks that randomisation, tracking and the analysis pipeline work correctly, and it reveals sample ratio mismatches or metric variance before real tests depend on the platform. Running many A/A tests also confirms your false positive rate is near 5%.
05
What is columnar storage and why do analytical warehouses use it?
Show answer
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.
06
What are additive, semi-additive and non-additive facts?
Show answer
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.
07
DAX: calculated column vs measure?
Show answer
A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.
08
You must present to the sales team that they missed their quarterly target by 18%. How do you handle it?
Show answer
Lead with the fact clearly, then show where the gap came from, such as which regions, products or deal stages, so it becomes actionable rather than a blame session. Separate controllable causes like lower conversion from uncontrollable ones like a delayed product launch. Highlight what did work, and end with two or three specific actions and the metrics to track them. Share the numbers with sales leaders privately first, to avoid surprises.
09
How do you make a dashboard accessible, for example for colour-blind users?
Show answer
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.
10
Write a Tableau calculated field for profit ratio and a profit category.
Show answer
// Profit Ratio (aggregate first, then divide)
SUM([Profit]) / SUM([Sales])
// Profit Category
IF SUM([Profit]) < 0 THEN 'Loss'
ELSEIF [Profit Ratio] < 0.1 THEN 'Low margin'
ELSE 'Healthy'
END
// avoid AVG([Profit]/[Sales]): it averages row ratios and gives a wrong total
11
What is the difference between ETL and ELT?
Show answer
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.
12
INNER JOIN vs LEFT JOIN — with an example.
Show answer
INNER JOIN returns only matching rows. LEFT JOIN returns every row from the left table and NULLs where the right table has no match — e.g. all customers, including those with no orders.
13
A marketing campaign shows more sales. How do you prove it worked?
Show answer
Compare with a control group or use difference-in-differences, control for seasonality, and measure incremental sales against campaign cost.
14
What is a role-playing dimension?
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.
15
What is an A/B test and why is random assignment so important?
Show answer
An A/B test is a controlled experiment where users are split into a control group that sees the current version and a treatment group that sees a change, and you compare a metric such as conversion. Random assignment makes the groups similar in every way except the change, including things you cannot measure. That is why a difference in results can be attributed to the change itself, not to user mix or seasonality.
16
What is a correlated subquery?
Show answer
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).
17
How do you calculate working days between two dates in Excel, excluding holidays?
Show answer
-- A2 = start date, B2 = end date, Holidays = named range of holiday dates
=NETWORKDAYS(A2, B2, Holidays)
-- if the weekend is only Sunday (weekend code 11)
=NETWORKDAYS.INTL(A2, B2, 11, Holidays)
-- date that is 10 working days after A2
=WORKDAY(A2, 10, Holidays)
18
Organic search traffic fell 40% overnight. How do you investigate?
Show answer
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.
19
What questions do you ask a stakeholder before building a dashboard?
Show answer
Who will use it and what decisions will they make with it? Which three to five questions must it answer? What are the KPI definitions, targets and comparison periods? Which data sources are trusted? How often will they look at it, and on what device? Who needs access and are there security restrictions? What does success look like in a month? I summarise the answers in a short brief and get sign-off before building.
20
What is a percentile and what is the interquartile range?
Show answer
A percentile is the value below which a given percentage of data falls; the 90th percentile delivery time means 90 percent of deliveries were faster. The interquartile range is the 75th percentile minus the 25th percentile, the spread of the middle half. IQR is robust to outliers, and values beyond 1.5 times the IQR from the quartiles are often flagged as outliers in box plots.
21
What are table calculations in Tableau?
Show answer
Table calculations are computed on the aggregated results already in the view, not on the underlying rows. Examples are running total, percent of total, difference from previous, rank and moving average. Their result depends on the direction and partition you set with Compute Using, for example table across or by specific dimensions. They are quick to add from Quick Table Calculation, but incorrect Compute Using settings are a frequent cause of wrong numbers.
22
How do you detect outliers?
Show answer
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.
23
What do COALESCE and NULLIF do in SQL?
Show answer
COALESCE returns the first non-null value from its arguments, for example COALESCE(phone, alt_phone, 'Not given') fills missing phone numbers. NULLIF(a, b) returns NULL when a equals b, otherwise a. Its classic use is avoiding division by zero: revenue / NULLIF(orders, 0) returns NULL instead of an error when orders is zero. Together they handle most null-related reporting problems cleanly.
24
Explain correlation vs regression.
Show answer
Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.
25
What is the difference between dimensions and measures in Tableau?
Show answer
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.
26
How do you choose the right chart?
Show answer
Line for trends, bar for comparisons, scatter for relationships, histogram for distributions and pie only for few parts of a whole.
27
Tell me about a time you migrated or rebuilt reports in a new BI tool.
Show answer
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.
28
What is the logical order in which SQL executes a SELECT query?
Show answer
Although written as SELECT first, a query is logically processed as FROM and JOIN, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because SELECT runs later, but you can use it in ORDER BY. It also explains why aggregates belong in HAVING, not WHERE, and why window functions cannot be filtered in the same query level.
29
What is the LAMBDA function in Excel and when would you use it?
Show answer
LAMBDA lets you write a custom, reusable function with formulas only, no VBA. You define it, for example =LAMBDA(price, rate, price*(1+rate)), and save it in the Name Manager as AddGST, then call =AddGST(B2, 18%) anywhere. Helper functions like MAP, REDUCE, SCAN and BYROW pair with it for array logic. Use it when a complex calculation repeats across a workbook, so the logic lives in one tested place.
30
What is hypothesis testing in simple words?
Show answer
You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.
31
How do you combine text from several cells with a separator in Excel?
Show answer
-- join first and last name with a space
=TEXTJOIN(" ", TRUE, A2, B2)
-- all products of one order in a single cell, comma separated
=TEXTJOIN(", ", TRUE, FILTER(C2:C500, A2:A500=F2))
-- older method
=A2 & " " & B2
-- TRUE in TEXTJOIN skips empty cells, which CONCAT and & do not
32
Why would you publish a Tableau data source separately instead of embedding it in each workbook?
Show answer
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.
33
How do you write a good hypothesis for an experiment?
Show answer
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.
34
What is cohort analysis?
Show answer
Grouping users by a shared start date or trait and tracking behaviour over time, for example monthly retention per signup month.
35
Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).
Show answer
-- 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
36
Write SQL using LAG to compare each month's revenue with the previous month.
Show answer
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month,
SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 1) AS mom_pct
FROM monthly
ORDER BY month;
37
How do you present insights to non-technical stakeholders?
Show answer
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.
38
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?
Show answer
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.
39
What is a p-value, explained simply?
Show answer
A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis, usually no effect, were true. A p-value of 0.03 means that if the new page truly made no difference, results this strong would appear only about 3 percent of the time by chance. Below a chosen threshold such as 0.05 we reject the null. It is not the probability that the hypothesis is true.
40
What is a north star metric?
Show answer
A north star metric is the single measure that best captures the value a product delivers to customers and predicts long-term growth, such as weekly active learners for an education app or orders delivered for a delivery service. It aligns teams around one outcome. It should be supported by input metrics that teams can influence, such as activation rate and repeat usage, and balanced with guardrails like cost or quality.
Tips to crack the Persistent Systems Data Analytics interview
- Practise coding problems of easy to medium difficulty, as Persistent assessments usually focus more on programming than service peers.
- Revise data structures, OOP, DBMS and operating system basics for the technical rounds.
- Read about Persistent's product engineering background and its partnerships with major cloud providers.
- 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; Persistent Systems interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Persistent Systems Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + coding); Technical round 1; Technical round 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 Persistent Systems Data Analytics interview?
This practice set leans on Excel (6), Statistics and Case Studies (6), Data Modelling & Warehousing (5), Tableau (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Persistent Systems interviewers ask you to walk through real work.
Are these the exact questions asked at Persistent Systems?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Persistent Systems's. They are not a leaked or verified list of past questions.
Where does Persistent Systems hire Data Analytics professionals in India?
Persistent Systems has large teams in Pune, Nagpur, Bengaluru, Hyderabad, Goa. Openings and locations change often, so check the Persistent Systems careers page and filter by city and experience.
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