YASH Technologies Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About YASH Technologies hiring for Data Analytics roles
YASH Technologies is an IT services and consulting company with a strong focus on SAP implementation, cloud, application development and digital transformation. It has its headquarters in the United States and a large presence in India, especially in Indore where it has major delivery centres. In India it hires SAP consultants, developers, testers and data engineers for enterprise client projects across industries.
For Data Analytics roles, this YASH Technologies practice set focuses most on SQL (6), Python for Analysis (5), Data Modelling & Warehousing (4), Data Storytelling (4). It has 15 easy, 24 medium, 1 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
East Moline, Illinois, USA
Main India offices
Indore, Pune, Hyderabad
YASH Technologies Data Analytics interview process
Online assessment (aptitude + technical MCQs)
Technical interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Write pandas code to fill missing values in a column with the median of each group.
# 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())
02
What questions do you ask a stakeholder before building a dashboard?
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.
03
What is columnar storage and why do analytical warehouses use it?
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.
04
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.
05
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.
06
Write pandas code to find the top 2 products by revenue in each region.
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.
08
What is the difference between a table and a normal range?
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.
10
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.
11
What are strategic, operational and analytical dashboards?
Strategic dashboards serve leadership with a few high-level KPIs and trends, updated daily to monthly. Operational dashboards monitor live or near-live activity for teams on the ground, such as orders pending dispatch or call queue length, often with alerts. Analytical dashboards let analysts and managers explore data with filters and drill-downs to find causes. Knowing which type you are building decides the level of detail, refresh rate and design.
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).
13
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.
Profile the data, remove exact duplicates using keys, impute missing values (mean/median/mode or a business rule), flag imputed rows and document every cleaning step.
15
What is the logical order in which SQL executes a SELECT query?
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.
16
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.
Data profiling is a quick systematic look at a dataset before analysis: row counts, data types, null percentages, distinct counts, minimum and maximum, common values and value patterns per column. In pandas, df.info(), df.describe(include='all') and value_counts() cover most of it; Power Query has column quality and distribution views. Profiling reveals problems like a date column stored as text or a status field with unexpected values early.
18
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.
It automatically formats cells based on rules such as colour scales, data bars or highlighting values above a target, making patterns visible at a glance.
20
What is the difference between a primary key and a unique constraint?
Both prevent duplicate values. A table can have only one primary key, it cannot contain NULLs, and it is the main identifier other tables reference through foreign keys. A table can have several unique constraints, for example on email and on PAN number, and most databases allow NULLs in a unique column. Use the primary key for the row identity and unique constraints for other business rules.
21
Tell me about a time your analysis was challenged in a meeting.
Situation: while presenting regional sales, a manager said my numbers were wrong because his team's tracker showed more. Task: handle it without derailing the meeting. Action: I acknowledged the concern, explained my source and definition briefly, and offered to reconcile after the meeting. That afternoon we found his tracker counted orders before cancellations. Result: we agreed on net orders as the standard, and he later asked me to rebuild his tracker.
22
What is the difference between a data warehouse, a data lake and a lakehouse?
A data warehouse stores cleaned, structured data in modelled tables optimised for SQL reporting. A data lake stores raw data of any type, such as logs, JSON and images, cheaply in files, with structure applied when read. A lakehouse combines them: open table formats on lake storage add transactions, schemas and fast SQL on top of files. Analysts typically query the warehouse or the curated layer of a lakehouse.
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.
24
What does value_counts do in pandas, and how do you show percentages instead of counts?
value_counts returns how many times each unique value appears in a Series, sorted from most to least frequent. It is a quick way to check category distribution or spot typos. Pass normalize=True to get proportions, then multiply by 100 for percentages, for example df['city'].value_counts(normalize=True).mul(100).round(1). Use dropna=False to include missing values, which are excluded by default and are often important.
25
What is a lambda function in Python and where do analysts use it?
A lambda is a small anonymous function written in one line, for example lambda x: x * 1.18 to add GST. Analysts use lambdas with sorted for custom sort keys, with pandas assign for computed columns, for example assign(margin=lambda d: d.profit / d.sales), and occasionally with apply. For anything longer than one expression or reused in several places, a normal def function is clearer and easier to test.
26
Why do ratio metrics need extra care on a dashboard compared with count metrics?
A ratio like conversion rate or average order value can move because the numerator or the denominator changed, so always show both components nearby. Ratios can look dramatic on small bases, such as 2 of 4 users. They also cannot be averaged across segments; you must divide total numerator by total denominator, otherwise small segments get too much weight. Counts are simpler but need context like trends or targets.
A Common Table Expression (WITH name AS (...)) is a named temporary result used inside one query. Use it to break complex logic into readable steps or for recursive queries.
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.
A chi-square test of independence checks whether two categorical variables are related, using a contingency table of counts. Example: is preferred payment method related to city tier? It compares observed counts with the counts expected if there were no relationship. Use it with counts, not percentages, and make sure expected counts are not too small, roughly at least 5 per cell. A significant result shows association, not causation.
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.
33
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.
34
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
35
Tell me about a time you found a mistake in a report after it had already been shared.
Situation: a day after sending a weekly report, I noticed one region's numbers included test orders. Task: correct it before decisions were made. Action: I quantified the impact, a 3 percent overstatement, and emailed the same audience with the corrected figures, a short explanation and the fix I had added to prevent repeats. Result: my manager appreciated the quick ownership, and the test-order filter became part of our shared dataset.
You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.
38
What is an A/B test and why is random assignment so important?
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.
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.
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
Tips to crack the YASH Technologies Data Analytics interview
Learn basic ERP and SAP concepts such as modules and business processes, since a large share of YASH work involves SAP.
Revise programming basics, SQL and DBMS concepts, and be ready to write simple queries or code during the technical round.
Show flexibility about technology and location, as freshers may be trained and placed on different stacks and projects.
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; YASH Technologies interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the YASH Technologies Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical MCQs); Technical interview; Managerial 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 YASH Technologies Data Analytics interview?
This practice set leans on SQL (6), Python for Analysis (5), Data Modelling & Warehousing (4), Data Storytelling (4). Revise those first, then practise explaining one or two of your own projects end to end, because most YASH Technologies interviewers ask you to walk through real work.
Are these the exact questions asked at YASH Technologies?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like YASH Technologies's. They are not a leaked or verified list of past questions.
Where does YASH Technologies hire Data Analytics professionals in India?
YASH Technologies has large teams in Indore, Pune, Hyderabad. Openings and locations change often, so check the YASH Technologies careers page and filter by city and experience.