Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About IBM hiring for Data Analytics roles
IBM is a global technology company offering hybrid cloud, AI through watsonx, software such as Red Hat, infrastructure and IBM Consulting services. Its India Software Labs and IBM Consulting teams form one of its largest workforces outside the US. It hires in India for software developers, test engineers, data engineers, data scientists and consultants.
For Data Analytics roles, this IBM practice set focuses most on Power BI (6), Web & Marketing Analytics (5), Excel (5), Statistics and Case Studies (4). It has 18 easy, 19 medium, 3 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Armonk, New York, USA
Main India offices
Bengaluru, Pune, Hyderabad, Kolkata, Gurugram
IBM Data Analytics interview process
Online assessment (cognitive games + coding)
English language test
Technical interview
Managerial or HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
What are statistical power and minimum detectable effect (MDE)?
Power is the probability that the test detects a real effect of a given size, commonly set at 80%. The minimum detectable effect is the smallest lift you care about and can reliably detect with your sample size. Smaller MDEs, lower baseline rates and higher power all need more users. If you can only get 10,000 users a week, you may only be able to detect large lifts, which should shape what you test.
02
Write SQL to find the first-touch and last-touch channel for every user who converted.
WITH touches AS (
SELECT t.user_id, t.channel, t.touch_ts,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts) AS first_rank,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts DESC) AS last_rank
FROM marketing_touches t
JOIN conversions c
ON c.user_id = t.user_id
AND t.touch_ts <= c.converted_ts
)
SELECT user_id,
MAX(CASE WHEN first_rank = 1 THEN channel END) AS first_touch,
MAX(CASE WHEN last_rank = 1 THEN channel END) AS last_touch
FROM touches
GROUP BY user_id;
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.
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.
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.
06
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.
07
How would you present the same churn analysis to the CEO and to the customer support team?
For the CEO, lead with the headline and money: churn rose 2 points, costing about 3 crore annually, mainly from one segment, with two recommended actions and their expected impact, all on one or two slides. For the support team, go deeper into drivers they control: ticket types, resolution times and specific customer pain points, with operational next steps and how progress will be tracked. Same data, different level and call to action.
08
Email open rates suddenly jumped from 22% to 45% without any campaign change. Should you celebrate?
No. Privacy features in some mail apps pre-load email images, which fires the open tracking pixel even if the person never reads the email, so open rates get inflated. Check opens by email client to confirm. Move reporting towards clicks, click-to-send rate, conversions and unsubscribes, which reflect real behaviour, and avoid using opens alone to pick winning subject lines or to define engaged subscribers.
09
What is the difference between a metric and a KPI?
A metric is any quantity you measure, such as page views, tickets closed or average order value. A KPI, key performance indicator, is a metric chosen because it directly reflects progress towards an important business goal and usually has a target and owner. Every KPI is a metric, but most metrics are not KPIs. A good dashboard leads with a few KPIs and keeps supporting metrics for diagnosis.
// 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])
12
Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.
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)
Row context evaluates one row at a time (calculated columns); filter context is the set of filters from slicers, visuals and relationships that a measure sees.
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.
16
What is a date table in Power BI and why should you mark it as one?
A date table has one row per day with no gaps, covering the full range of your data, plus columns like year, month, quarter and week. Marking it as a date table tells Power BI to use it for time intelligence functions such as TOTALYTD and SAMEPERIODLASTYEAR. Without it, those functions can give wrong results and auto date tables bloat the model. Relate it to every fact table's date column.
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.
19
What are primary metrics and guardrail metrics in an A/B test?
The primary metric is the one decision metric the test is designed to move, such as checkout conversion. Guardrail metrics are ones that must not get worse, such as page load time, refund rate, unsubscribe rate or revenue per user. A change that lifts conversion but increases cancellations should not ship. Define both before launch, so the decision rule is clear and nobody picks the favourable metric afterwards.
An action title states the insight of a chart or slide as a sentence instead of a label. Instead of Monthly Sales by Region, write South region drove 70 percent of the decline since July. Busy executives may read only the titles, so each one should carry the message and, ideally, the so-what. The chart below then proves the title. Read only your titles in order: they should tell the whole story.
SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
23
What are pre-attentive attributes and how do you use them in charts?
Pre-attentive attributes are visual properties the brain notices in a fraction of a second before conscious thought, such as colour, size, position, length and bold weight. Use them to direct attention: make all bars grey and highlight only the declining region in a strong colour, or bold the one number that matters. Using too many highlights cancels the effect, so emphasise one or two things per chart.
24
Your Power BI dataset with five years of transactions takes two hours to refresh. How would you fix it?
Incremental refresh partitions a large table by date so that each refresh reloads only recent data, for example the last 7 days, while older partitions stay untouched. You create RangeStart and RangeEnd parameters, filter the table with them in Power Query, and set a policy such as store 5 years, refresh 7 days. Refresh becomes faster and lighter on the source. Query folding must work for the filter to be pushed to the database.
Incrementality testing measures sales that happened because of a campaign, compared with what would have happened anyway. You hold out a random group of users or a set of matched cities from the campaign and compare their conversions with the exposed group. Attribution tools often over-credit channels like retargeting and brand search, which reach people already likely to buy, so incrementality tests show the true lift and the true cost per incremental sale.
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.
27
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.
28
What are variables (VAR) in DAX and why are they useful?
VAR stores the result of an expression inside a measure, and RETURN gives the final output. Variables make measures readable, avoid evaluating the same expression twice, and are evaluated in the filter context where they are defined, which helps avoid subtle bugs inside CALCULATE. They also make debugging easy, because you can temporarily RETURN a variable to check its value before returning the final result.
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.
30
What is the normal distribution and the 68-95-99.7 rule?
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.
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.
A Type I error is a false positive: concluding there is an effect when there is none, for example launching a feature that does not actually lift sales. Its probability is alpha, often 0.05. A Type II error is a false negative: missing a real effect, with probability beta. Power is 1 minus beta. Larger samples reduce Type II errors, and the business cost of each mistake should guide which one you guard against more.
33
What is a percentile and what is the interquartile range?
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.
34
Your event logs store timestamps in UTC, but the business reports daily sales in IST, and late-night orders land on the wrong day. How do you fix it?
Convert timestamps to the business time zone before deriving the date. IST is UTC plus 5 hours 30 minutes, so an order at 20:00 UTC belongs to the next day in IST. In SQL use AT TIME ZONE or CONVERT_TZ, and in pandas use tz_localize('UTC') followed by tz_convert('Asia/Kolkata'). Store raw UTC, add a local date column in the model, and document which one each report uses.
MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.
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.
37
What is the difference between a live connection and an extract in Tableau?
A live connection queries the source database every time a user interacts, so data is always current but performance depends on the database. An extract is a compressed snapshot stored in Tableau's hyper format, usually much faster, refreshed on a schedule and usable offline. Use live for near real-time needs on a fast warehouse, and extracts for speed, slow sources or when you want to reduce load on production systems.
A KPI tree breaks a top-level metric into the drivers that multiply or add up to it. Revenue = traffic x conversion rate x average order value, and each branch splits further, such as traffic by channel. When revenue drops, the tree shows which driver moved, so investigations are structured rather than random. It also links teams to the branch they own and helps choose leading indicators for a dashboard.
40
You inherit a monthly Excel report and suspect some numbers are hard-typed over formulas. How do you audit it?
Use Go To Special and select Constants with Numbers to highlight hard-typed values inside formula areas. Show Formulas (Ctrl+`) to scan columns for inconsistencies, and use Trace Precedents and Dependents on key totals. Inquire or Spreadsheet Compare, if available, can flag inconsistent formulas. Then fix the overwritten cells, protect formula ranges and add check totals that compare with the source data each month.
Tips to crack the IBM Data Analytics interview
Practise IBM's game-based cognitive assessments, which test pattern recognition, memory and numerical reasoning quickly.
Learn basics of hybrid cloud, Red Hat OpenShift and watsonx, which IBM promotes as core strategic areas.
Revise SQL, Python or Java fundamentals and be ready to explain one project in depth.
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; IBM interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the IBM Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (cognitive games + coding); English language test; Technical interview; Managerial or 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 IBM Data Analytics interview?
This practice set leans on Power BI (6), Web & Marketing Analytics (5), Excel (5), Statistics and Case Studies (4). Revise those first, then practise explaining one or two of your own projects end to end, because most IBM interviewers ask you to walk through real work.
Are these the exact questions asked at IBM?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like IBM's. They are not a leaked or verified list of past questions.
Where does IBM hire Data Analytics professionals in India?
IBM has large teams in Bengaluru, Pune, Hyderabad, Kolkata, Gurugram. Openings and locations change often, so check the IBM careers page and filter by city and experience.