Fractal Analytics Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Fractal Analytics hiring for Data Analytics roles
Fractal Analytics is an AI and analytics company working with large global enterprises in consumer goods, retail, healthcare and financial services. It provides data science, data engineering and AI products. In India it hires for data analysts, data scientists, data engineers, ML engineers and analytics consultants.
For Data Analytics roles, this Fractal Analytics practice set focuses most on Dashboards and KPIs (7), Power BI (7), Data Cleaning (5), SQL (4). It has 9 easy, 28 medium, 3 hard questions, so you can start easy and work up to the harder ones.
Company type
Analytics
Headquarters
Mumbai, India
Main India offices
Mumbai, Bengaluru, Gurugram, Pune
Fractal Analytics Data Analytics interview process
Online assessment (aptitude + SQL + Python or statistics)
Technical round 1
Technical round 2 or case round
Hiring manager round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Orders arrive in the warehouse with customer IDs that do not exist yet in the customer dimension. What do you do?
These are late-arriving dimensions. Do not drop the orders, or revenue will be understated. Common practice is to insert a placeholder customer row with the natural key and 'Unknown' attributes, link the order to it, and update that row when full details arrive. Alternatively map to a single Unknown member with key -1. Also monitor how many such rows appear daily, because a spike usually means a broken feed.
02
What are leading and lagging indicators? Give examples.
Lagging indicators report outcomes that have already happened, such as monthly revenue, profit or churn; they are accurate but too late to change. Leading indicators move earlier and predict outcomes, such as qualified leads, demo bookings, cart additions or support ticket backlog. A sales dashboard should pair them: pipeline coverage today signals revenue next quarter. Leading indicators need validation that they really predict the lagging result.
03
A file has dates in mixed formats, some dd/mm/yyyy and some mm/dd/yyyy. How do you handle it?
Find out which system or region produced each format, since a source column often explains it. Values with a day above 12 reveal the format unambiguously. Parse with an explicit format per source, for example pd.to_datetime(col, format='%d/%m/%Y', errors='coerce'), rather than letting the tool guess. Check failed or suspicious parses, such as future dates, and ask the source team to send ISO format yyyy-mm-dd going forward.
04
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
05
What is the difference between a calculated table in DAX and a table created in Power Query?
A Power Query table is built during data refresh using M, before data enters the model, and is compressed like any imported table. A DAX calculated table is computed after load from other model tables, using functions such as CALENDAR, SUMMARIZE or DISTINCT. Prefer Power Query for shaping and cleaning source data. Use calculated tables for model-dependent needs, such as a date table from existing data or a small what-if table.
06
How do you unpivot a wide monthly sales sheet into a long table in Power Query?
Load the table into Power Query. Select the identifier columns such as Region and Product, then choose Transform, Unpivot Other Columns. The month columns become two columns, Attribute and Value; rename them Month and Sales. Change Month to a date type and Sales to a decimal number. Using Unpivot Other Columns rather than Unpivot Columns means new months added later are picked up automatically on refresh.
07
Write pandas code for a pivot table of sales by region and category with sum and count.
This is an encoding mismatch: a UTF-8 file was read as a different encoding such as Latin-1 or Windows-1252. Re-read it with the right encoding, for example pd.read_csv(path, encoding='utf-8') or 'utf-8-sig' for files with a byte order mark, or set the file origin in Excel and Power Query import. Avoid re-saving through tools that change encoding, and agree on UTF-8 for all exchanges, especially for Hindi or other Indian language text.
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.
12
How do you handle null values in Tableau calculations?
// treat missing sales as zero
ZN(SUM([Sales]))
// replace a missing value with a default
IFNULL([Region], 'Unknown')
// test explicitly
IF ISNULL([Discount]) THEN 0 ELSE [Discount] END
// right-click a null indicator on an axis to filter or show nulls at default position
13
How do you handle a dataset with 30% missing values in a key column?
Understand why it is missing, consider imputation or a missing flag, test sensitivity, and talk to data owners about fixing collection if it is systematic.
14
A retail chain asks you which of its 120 stores it should close. How do you approach it?
Clarify the goal first: profit, cash or market share. Build a store-level dataset with revenue, contribution margin after rent and staff, trend over 24 months, footfall, catchment demographics and cannibalisation with nearby stores. Separate structural losers from stores that are new or temporarily disrupted. Estimate how much revenue would shift to nearby stores or online if a store closes. Present a shortlist with scenarios and risks, not a single answer.
15
What KPIs would you track for a customer support call centre?
Volume and staffing: calls offered, answered, abandoned rate and average speed of answer. Efficiency: average handle time and occupancy. Quality and outcome: first contact resolution, customer satisfaction or CSAT score, and repeat contacts within 7 days. Service level, for example 80 percent of calls answered in 20 seconds, ties them together. Balance speed metrics with quality, otherwise agents rush calls to cut handle time.
16
Numbers in an Excel column are stored as text and SUM returns zero. How do you fix it?
Signs are left-aligned numbers, green triangles, and COUNT returning less than COUNTA. Fix options: select the column and use the warning menu Convert to Number; use Data, Text to Columns and Finish; or a helper column with =VALUE(TRIM(A2)). If there are hidden characters, use =VALUE(SUBSTITUTE(TRIM(A2), CHAR(160), "")) to remove non-breaking spaces from web exports. In Power Query, simply change the column type to number.
17
After a website migration, the share of direct traffic jumped from 15% to 45%. What could cause it?
Direct is the default bucket when the source is unknown, so a jump usually means attribution broke. Common causes are redirects that strip UTM parameters, a new referrer policy hiding the referring site, the analytics tag missing on landing pages so sessions start later on the site, payment or login domains not added to the cross-domain or referral exclusion list, and HTTPS to HTTP transitions. Compare landing pages and campaign traffic before and after.
18
Your findings deck has 30 slides and you have 10 minutes with leadership. How do you cut it down?
Write the single key message first, then keep only slides that directly support it, usually three to five: the headline finding, the main driver, the impact in money, and the recommendation with next steps. Merge related charts, use action titles, and move everything else to an appendix for questions. Rehearse to fit in about seven minutes, leaving time for discussion, because the discussion is where decisions are made.
19
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.
20
Your A/B test ends with no significant difference. How do you report it?
A flat result is still useful. Report the observed difference with its confidence interval, for example -0.3% to +0.8%, which shows the range of effects still possible. State whether the test had enough power for the planned MDE. If it did, conclude that any effect is probably small and the team can choose based on cost or simplicity. Share learnings so the same idea is not retested blindly.
21
Tell me about a time a dashboard you built changed a business decision.
Situation: our operations team assumed late deliveries were evenly spread. Task: I built a delivery performance dashboard. Action: I added a breakdown by courier partner and pin code cluster with a weekly trend, and reviewed it with the operations manager. Result: it showed one partner caused 60 percent of delays in two zones. The company shifted those zones to another partner, and on-time delivery rose from 84 to 92 percent in two months.
22
Tell me about a time you used data to challenge an assumption.
Situation: our team believed weekend discounts drove most sales. Task: I was preparing the monthly review. Action: I compared discount and non-discount weekends over six months and controlled for paydays and festivals. Discounts moved sales forward from the following week but added little overall. I presented it with one clear chart. Result: management trialled fewer discount weekends, and margin improved without a fall in monthly revenue.
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
24
Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.
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.
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.
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.
A dataflow is Power Query running in the Power BI Service. It extracts and cleans data once and stores the result so many datasets can reuse it. For example, a single cleaned Customer table can feed sales, finance and marketing reports, ensuring consistent logic and reducing load on the source. Dataflows suit shared, reusable preparation; logic that only one report needs can stay in that report's Power Query.
34
A profit margin visual shows errors for products with zero sales. How do you fix it in DAX?
DIVIDE(numerator, denominator, alternate) handles division by zero or blank safely and returns blank or the alternate value you supply, so visuals do not show errors or infinity. The / operator returns an error or infinity in those cases. For example, DIVIDE([Profit], [Sales]) shows blank for a product with no sales. Use / only when you are certain the denominator can never be zero, because it is marginally faster.
35
Tell me about a time you disagreed with your manager about how to measure something.
Situation: my manager wanted to report average resolution time for support tickets. Task: I felt a few very old tickets distorted the average. Action: I prepared a quick comparison showing mean versus median and the percentage resolved within 24 hours, and explained why the median was more representative. Result: we agreed to show median and the 24-hour rate, keeping the mean in a tooltip, and the team stopped chasing misleading spikes.
A bus matrix is a planning grid with business processes as rows, such as orders, shipments and returns, and conformed dimensions as columns, such as date, customer, product and warehouse. A tick shows which dimensions each process uses. It helps prioritise which fact tables to build, shows which dimensions must be shared and conformed first, and gives leaders a one-page roadmap of the warehouse without technical detail.
37
What is the difference between COUNT(*), COUNT(column) and COUNT(DISTINCT column)?
COUNT(*) counts all rows, including rows with NULLs. COUNT(column) counts only rows where that column is not NULL. COUNT(DISTINCT column) counts unique non-null values. For example, in an orders table, COUNT(*) gives number of orders, COUNT(coupon_code) gives orders that used a coupon, and COUNT(DISTINCT customer_id) gives number of unique buyers. Mixing these up is a common source of wrong KPIs.
38
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.
39
Write SQL to show each category's percentage share of total revenue.
SELECT category,
SUM(amount) AS revenue,
ROUND(100.0 * SUM(amount)
/ SUM(SUM(amount)) OVER (), 2) AS pct_share
FROM sales
GROUP BY category
ORDER BY revenue DESC;
-- SUM(SUM(amount)) OVER () is the grand total across all groups
40
Analytics shows 1,820 purchases last month but the order database shows 2,150. Which is right, and how do you explain the gap?
The order database is the source of truth for counts and revenue. Analytics usually undercounts because of ad blockers, declined cookie consent, users closing the page before the confirmation tag fires, and payment redirects that break sessions. It can also overcount from duplicate tags on page refresh. Quantify the gap per month; if it is stable around 15%, use analytics for channel mix and trends and the database for totals.
Tips to crack the Fractal Analytics Data Analytics interview
Revise statistics, machine learning basics and SQL, and be ready to explain model choice and evaluation metrics.
Practise analytics case questions for consumer goods or retail clients, such as forecasting or pricing.
Read about Fractal's AI products and its focus on decision intelligence for company-related 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; Fractal Analytics interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Fractal Analytics Data Analytics interview?
Candidates usually go through 5 stages: Online assessment (aptitude + SQL + Python or statistics); Technical round 1; Technical round 2 or case round; Hiring manager round; 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 Fractal Analytics Data Analytics interview?
This practice set leans on Dashboards and KPIs (7), Power BI (7), Data Cleaning (5), SQL (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Fractal Analytics interviewers ask you to walk through real work.
Are these the exact questions asked at Fractal Analytics?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Fractal Analytics's. They are not a leaked or verified list of past questions.
Where does Fractal Analytics hire Data Analytics professionals in India?
Fractal Analytics has large teams in Mumbai, Bengaluru, Gurugram, Pune. Openings and locations change often, so check the Fractal Analytics careers page and filter by city and experience.