Quest Global Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Quest Global hiring for Data Analytics roles
Quest Global is an engineering services company headquartered in Singapore with large engineering centres in India. It provides mechanical design, embedded software, digital engineering and manufacturing support to clients in aerospace, energy, healthcare, automotive and semiconductors. In India it hires mechanical, electrical, electronics and software engineers, with centres in cities such as Bengaluru, Thiruvananthapuram and Belagavi.
For Data Analytics roles, this Quest Global practice set focuses most on SQL (5), Python for Analysis (5), Data Cleaning (5), Data Modelling & Warehousing (4). It has 24 easy, 15 medium, 1 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Singapore
Main India offices
Bengaluru, Thiruvananthapuram, Belagavi, Pune
Quest Global 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
What is a holdout group and why do teams keep one?
A holdout group is a small share of users, often 5-10%, kept out of a feature or of all marketing campaigns for a long time. Comparing them with everyone else shows the cumulative long-term impact, which individual short tests miss, and checks whether many small wins really add up. For example, a notification holdout shows whether push messages raise monthly retention or just shift activity earlier.
02
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.
03
What is the difference between loc and iloc in pandas?
loc selects by labels: row index labels and column names, for example df.loc[df['city'] == 'Pune', ['name', 'sales']]. iloc selects by integer position, for example df.iloc[0:5, 0:3] for the first five rows and three columns. With loc, slices include the end label; with iloc, the end position is excluded like normal Python. Use loc for readable filtering and iloc when position matters.
04
What are named ranges in Excel and why are they useful?
A named range gives a cell or range a meaningful name, such as GST_Rate or SalesData, managed from the Name Manager. Formulas become readable, for example =Amount*GST_Rate instead of =B2*$H$1, and they do not break when you move the input cell. Names also work as data validation sources and in charts. For growing data, prefer Excel Tables, whose structured references expand automatically.
05
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.
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.
07
A report filters WHERE order_date = '2026-03-31' and misses almost all orders from that day. Why?
order_date is probably a DATETIME or TIMESTAMP column, so the filter matches only rows at exactly midnight, 2026-03-31 00:00:00. Use a half-open range instead: WHERE order_date >= '2026-03-31' AND order_date < '2026-04-01'. This keeps the index usable, unlike wrapping the column in CAST or DATE(), and it correctly includes orders at 23:59:59.500. Also check the time zone of the stored timestamps.
08
Why does correlation not imply causation? Give a business example.
Two variables can move together because of a third factor, reverse causation or pure chance. Example: stores with more staff have higher sales, but busy locations get more staff because they already sell more, so hiring alone may not raise sales. Ice-cream sales and AC sales both rise in summer because of temperature. To claim causation you need experiments, natural experiments or careful control of confounders.
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.
10
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.
11
What is DAX and how does it differ from Excel formulas?
Drag Order Date to Columns as a continuous or discrete month, put Sales on Rows, then drag Profit Ratio to Rows next to it. Right-click the second pill and choose Dual Axis. On the Marks card set Sales to Bar and Profit Ratio to Line. Decide whether to synchronise axes: do not synchronise when units differ, like rupees and percentage. Label the axes clearly so readers know which is which.
Tableau Prep is a visual tool for cleaning and shaping data before analysis. You build a flow of steps: connect inputs, clean fields, split columns, group similar values, pivot, join, union and aggregate, and see the data change at every step. The output can be an extract or published data source that dashboards use. It suits analysts who need repeatable preparation without writing SQL or Python.
14
What is the difference between COUNT, COUNTA and COUNTIF?
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.
16
Write pandas code to compute month-on-month percentage change and cumulative sales for each store.
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
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.
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).
19
When would you drop missing values, and when would you impute with mean or median?
Drop rows when few are missing, roughly under 5 percent, and missingness looks random. Impute when dropping would lose too much data. Use the median for skewed numbers such as income or order value, the mean for roughly symmetric data, and the mode or an Unknown category for categoricals. Group-wise imputation, such as median by city, is better than one global value. Always add a flag column showing what was imputed.
20
What is the difference between population and sample?
A simple structure is context, conflict, resolution. Context sets up what the audience already knows, such as our target was 10 percent growth. Conflict shows the surprising finding or problem, such as growth is only 3 percent because repeat buyers dropped. Resolution gives the recommendation and expected impact. Another option is situation, complication, question, answer. Either way, the audience should know the main message within the first minute.
A z-score tells how many standard deviations a value is from the mean: z = (x minus mean) divided by standard deviation. A delivery time with z = 2.5 is unusually slow. Analysts use z-scores to flag outliers, often beyond plus or minus 3, to compare values on different scales, such as marks in different exams, and to look up probabilities when data is roughly normal.
23
What is the difference between concat, merge and join in pandas?
concat stacks DataFrames vertically (more rows) or side by side along an axis, for example appending monthly files with the same columns. merge combines DataFrames on key columns, like a SQL join, with how='inner', 'left', 'right' or 'outer'. join is a shortcut for merging on the index. Use concat to append and merge to look up related data. Always check row counts after a merge.
UTM parameters are tags added to a URL so analytics tools know where a visit came from. The main ones are utm_source (instagram, newsletter), utm_medium (paid_social, email, cpc), utm_campaign (diwali_sale_2026), plus optional utm_content and utm_term. Without them, traffic from emails, messaging apps and many social links shows up as direct or referral, and you cannot measure which campaign drove sign-ups or sales.
25
What is the difference between a clustered and a non-clustered index?
Situation: while building a returns report I noticed one warehouse had zero returns for three months. Task: confirm whether it was real. Action: I compared with the source system and found its return codes had changed after a software update, so my filter missed them. I informed the data owner, updated the mapping and back-filled the history. Result: the corrected report showed that warehouse had the highest return rate, which led to a packaging review.
27
When would you use a one-tailed test instead of a two-tailed test?
A two-tailed test checks for a difference in either direction and is the safer default for A/B tests. A one-tailed test checks only one direction, such as treatment being better, and needs fewer users. Use it only if you decided beforehand that you would act the same way whether the change has no effect or a negative effect. Otherwise you may miss harm, which is why most teams stay two-tailed.
28
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
Orphan records are child rows whose foreign key has no matching parent, such as orders with a customer_id that does not exist in the customer table. They break joins silently: an inner join drops them and totals fall short. Find them with a LEFT JOIN from the child to the parent and filter where the parent key IS NULL. Then fix the source, add an Unknown member, or report them separately.
29
Write SQL to get each order's customer city as it was on the order date, using an SCD Type 2 customer dimension.
-- dim_customer has valid_from, valid_to (NULL for current row)
SELECT o.order_id,
o.order_date,
c.customer_id,
c.city
FROM fact_orders o
JOIN dim_customer c
ON c.customer_id = o.customer_id
AND o.order_date >= c.valid_from
AND o.order_date < COALESCE(c.valid_to, DATE '9999-12-31');
30
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.
31
Write pandas code to trim whitespace and lower-case every text column in a DataFrame.
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.
33
During a review, an executive asks a question about your numbers that you cannot answer on the spot. What do you do?
Do not guess. Say clearly that you do not have that cut ready, restate the question to confirm it, and give a time by which you will reply, for example by end of day. If you know a partial answer or a reasonable expectation, share it with a caveat. Then follow up on time with a short, precise answer. Being honest and reliable builds more trust than a confident wrong number.
34
Write a UTM-tagged URL for a paid Instagram campaign promoting a summer sale, and give two naming rules.
https://www.example.com/sale?utm_source=instagram&utm_medium=paid_social&utm_campaign=summer_sale_2026&utm_content=reel_video_a
-- Rule 1: always lower case with underscores; 'Instagram' and 'instagram' become separate rows
-- Rule 2: keep a shared UTM sheet with allowed source and medium values so every team tags the same way
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
A natural key comes from the source system and has business meaning, such as a customer mobile number or product SKU. A surrogate key is a meaningless integer or hash generated in the warehouse. Surrogate keys protect the model when source keys change, get reused or clash across systems, and they are essential for slowly changing dimensions where one customer has several historical rows. Keep the natural key as an attribute for lookups.
38
Write pandas code to extract month name, weekday and hour from a timestamp column.
Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.
40
What is data cleaning and why does it take so much of an analyst's time?
Data cleaning means fixing or removing incorrect, incomplete, duplicated or inconsistent data so analysis is trustworthy. Real data comes from many systems and manual entry, so you meet typos, mixed formats, missing values and duplicates. Analysts often spend half or more of a project on it because every later chart and model depends on it. Wrong inputs give confident but wrong answers, which is worse than no answer.
Tips to crack the Quest Global Data Analytics interview
Revise core subjects from your engineering branch, such as machine design, electronics or programming, depending on the role applied for.
Be ready to discuss tools you have used, such as CAD software, MATLAB or a programming language, with concrete project examples.
Read about Quest Global's industries like aerospace and energy so you can explain why engineering services appeal to you.
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; Quest Global interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Quest Global 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 Quest Global Data Analytics interview?
This practice set leans on SQL (5), Python for Analysis (5), Data Cleaning (5), Data Modelling & Warehousing (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Quest Global interviewers ask you to walk through real work.
Are these the exact questions asked at Quest Global?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Quest Global's. They are not a leaked or verified list of past questions.
Where does Quest Global hire Data Analytics professionals in India?
Quest Global has large teams in Bengaluru, Thiruvananthapuram, Belagavi, Pune. Openings and locations change often, so check the Quest Global careers page and filter by city and experience.