01
What is a CTE and why use it?
Show answer
A Common Table Expression is a named temporary result defined with WITH that makes complex queries readable and allows recursion.
Protiviti Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Protiviti hiring for Data Analytics roles
Protiviti is a global consulting firm that is a subsidiary of Robert Half. It specialises in internal audit, risk and compliance, technology consulting, cybersecurity, data analytics and business process improvement. In India it operates through a member firm and hires analysts and consultants for risk advisory, IT audit, security, finance transformation and technology implementation roles for clients across many sectors.
For Data Analytics roles, this Protiviti practice set focuses most on SQL (8), Tableau (5), Python for Analysis (4), Web & Marketing Analytics (4). It has 6 easy, 30 medium, 4 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Consulting
- Headquarters
- Menlo Park, California, USA
- Main India offices
- Gurugram, Mumbai, Bengaluru, Hyderabad
Protiviti Data Analytics interview process
- Resume screening or online assessment
- Technical interview
- Managerial round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
Find the second highest salary in SQL.
Show answer
SELECT MAX(salary) FROM emp
WHERE salary < (SELECT MAX(salary) FROM emp);
-- or: DENSE_RANK() OVER (ORDER BY salary DESC) = 2
03
Explain row context and filter context.
Show answer
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.
04
How do you calculate a running total?
Show answer
SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.
05
Write pandas code to standardise inconsistent city names such as 'delhi ', 'New Delhi' and 'DELHI'.
Show answer
city_map = {
'delhi': 'Delhi',
'new delhi': 'Delhi',
'bombay': 'Mumbai',
'mumbai': 'Mumbai',
'bengaluru': 'Bengaluru',
'bangalore': 'Bengaluru',
}
clean = df['city'].str.strip().str.lower().str.replace(r'\s+', ' ', regex=True)
df['city_clean'] = clean.map(city_map).fillna(clean.str.title())
print(df['city_clean'].value_counts())
06
When do you use a chi-square test?
Show answer
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.
07
What is the difference between a view and a materialised view?
Show answer
A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.
08
What is the difference between SUM and SUMX in DAX?
Show answer
SUM adds up a single column in the current filter context. SUMX is an iterator: it goes row by row over a table, evaluates an expression for each row, then sums the results. Use SUMX when the value must be computed per row first, for example SUMX(Sales, Sales[Qty] * Sales[Price]). Iterators are flexible but can be slower on very large tables, so storing a simple column is sometimes better.
09
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).
10
Write SQL to calculate users, conversions and conversion rate for each test variant.
Show answer
SELECT
a.variant,
COUNT(DISTINCT a.user_id) AS users,
COUNT(DISTINCT o.user_id) AS converters,
ROUND(100.0 * COUNT(DISTINCT o.user_id)
/ COUNT(DISTINCT a.user_id), 2) AS conversion_pct
FROM experiment_assignments a
LEFT JOIN orders o
ON o.user_id = a.user_id
AND o.order_ts >= a.assigned_ts
WHERE a.experiment_id = 'checkout_v2'
GROUP BY a.variant;
11
Your scheduled Python report failed silently and nobody noticed for a week. How do you make it reliable?
Show answer
Add logging to a file or central log, wrap the main steps in try and except that send an alert by email or chat on failure, and exit with a non-zero code so the scheduler marks the job failed. Add data checks, such as row counts above a minimum and no missing dates, before sending output. Use a proper scheduler or orchestrator with retries, and include the data date in every report.
12
Write SQL to find the first-touch and last-touch channel for every user who converted.
Show answer
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;
13
What is an action title and why should you use one?
Show answer
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.
14
What is the difference between extract filters and data source filters in Tableau?
Show answer
Extract filters decide which rows go into the extract file at all, which reduces its size and speeds every view; filtered rows are simply not available. Data source filters apply to all worksheets using that data source, for live or extract, and are useful for security or scope, such as only the India business. Both act before context filters. Normal worksheet filters only affect one sheet.
15
Estimate how many cups of tea are sold in a city of 1 crore people in a day.
Show answer
State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.
16
What is the difference between a calculated field and a helper column in Excel?
Show answer
A calculated field lives inside a PivotTable and works on summarised data; a helper column is a normal column of formulas that feeds the PivotTable or other formulas.
17
After a website migration, the share of direct traffic jumped from 15% to 45%. What could cause it?
Show answer
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
How would you present the same churn analysis to the CEO and to the customer support team?
Show answer
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.
19
Traffic suddenly doubled from one city, with near-zero engagement time and no conversions. What is happening and what do you do?
Show answer
This is very likely bot or spam traffic, or a misfiring tag. Check the user agents, IP ranges, hostnames, screen resolutions and landing pages for that traffic. If it is bots, exclude it with filters or data segments in analytics and in your reporting layer, and inform the web team so they can block it. Then restate the affected dashboard numbers, because bots inflate sessions and pull down conversion rate.
20
Write pandas code for a pivot table of sales by region and category with sum and count.
Show answer
import pandas as pd
summary = pd.pivot_table(
df,
index='region',
columns='category',
values='sales',
aggfunc=['sum', 'count'],
fill_value=0,
margins=True # adds an All row and column
)
print(summary)
21
Tell me about a time you retired or consolidated reports that nobody used.
Show answer
Situation: our team maintained over 60 scheduled reports and spent two days a month fixing them. Task: reduce the load without upsetting users. Action: I checked usage logs, listed reports not opened in 90 days, contacted owners and merged overlapping ones into four dashboards with filters. I archived the rest with a 30-day notice. Result: we retired 38 reports, only two were requested back, and maintenance time halved.
22
What are the main dimensions of data quality?
Show answer
Common dimensions are accuracy, values reflect reality; completeness, required fields are present; consistency, the same fact matches across systems; validity, values follow formats and rules such as a 6-digit pin code; uniqueness, no unwanted duplicates; and timeliness, data is fresh enough for its use. Measuring these with simple checks, like percent missing or duplicate rate, turns vague complaints about bad data into fixable issues.
23
Tell me about a time tracking broke and you had to fix the reporting.
Show answer
Situation: after a checkout redesign, purchases in our analytics fell 35% overnight while orders were normal. Task: find the cause and protect the weekly report. Action: I compared analytics with the order table by page and device, found the purchase tag no longer fired on the new UPI payment success page, and worked with developers to fix it. Result: I backfilled the gap from order data, added a footnote and set up a daily mismatch alert.
24
How do you remove duplicates in Excel but keep the latest record?
Show answer
Sort by date (newest first), then Data → Remove Duplicates on the key column — Excel keeps the first occurrence. Or use UNIQUE / a COUNTIFS helper column.
25
Which KPIs would you include in an HR attrition dashboard?
Show answer
Core KPIs: headcount, monthly and annualised attrition rate, voluntary versus involuntary split, and early attrition within the first 90 days or first year. Breakdowns by department, location, manager, tenure band, performance rating and salary band show where it concentrates. Add hiring metrics such as time to fill and offer acceptance rate, and exit reasons from exit interviews. Protect privacy by hiding very small groups.
26
Your workbook has 200,000 rows and hundreds of VLOOKUPs and takes minutes to recalculate. What do you do?
Show answer
First switch calculation to manual to work safely, then find the cause. Replace whole-column references with Tables, swap repeated VLOOKUPs for one XLOOKUP or INDEX-MATCH helper column, and remove volatile functions like OFFSET, INDIRECT and TODAY used in many cells. Better still, move the joins into Power Query or the Data Model so lookups happen once at refresh. Finally paste values for historic data that never changes and keep formulas only where needed.
27
What is a PivotTable and when do you use it?
Show answer
A tool that summarises large tables by dragging fields into rows, columns, values and filters, so you can slice sales by region or month without writing formulas.
28
A report filters WHERE order_date = '2026-03-31' and misses almost all orders from that day. Why?
Show answer
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.
29
What types of dashboard actions does Tableau offer?
Show answer
Filter actions filter other sheets when a user selects or hovers on a mark, such as clicking a region to filter a product table. Highlight actions emphasise related marks without removing others. URL actions open a web page with field values in the link. Go to Sheet navigates to another dashboard. Parameter and set actions update a parameter or set based on selection, enabling advanced interactions like dynamic drill-downs and comparisons.
30
Your 50/50 test has 52,400 users in control and 47,600 in treatment. What does this tell you?
Show answer
This is a sample ratio mismatch. A chi-square test on these counts gives a tiny p-value, so the gap is not chance. Something is filtering users unevenly, such as the treatment page crashing on some devices, a redirect losing tracking, bots in one arm or a bug in assignment. The results cannot be trusted until the cause is found. Check splits by browser, platform and day to locate it.
31
Tell me about a time you handled a request for access to sensitive data.
Show answer
Situation: a regional manager asked for a salary-level export of all employees to plan hiring. Task: help without breaking data policy. Action: I clarified the actual need, which was headcount cost by grade and location, checked with HR on access rules, and built a Power BI view with aggregated costs and row-level security limiting each manager to their region. Result: the manager got what was needed and no individual salaries were exposed.
32
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.
33
What is a snowflake schema and when would you choose it over a star schema?
Show answer
A snowflake schema normalises dimensions into several related tables, for example product linked to subcategory linked to category, instead of keeping one flat product dimension. It saves some storage and avoids repeating attribute values, but needs more joins and is harder for users and BI tools. Most analytics teams prefer a star schema; snowflaking makes sense when a hierarchy is huge, shared by many dimensions or maintained by a separate team.
34
Tell me about a time a stakeholder rejected your first dashboard design.
Show answer
Situation: I built a Tableau operations dashboard, and the warehouse head said it was too complex to use on the floor. Task: make it useful for them. Action: I watched how supervisors worked, learned they needed three numbers on a tablet every hour, and rebuilt it with large KPI tiles and one drill-down. Result: daily usage went from a handful of users to every shift supervisor within two weeks.
35
Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?
Show answer
A context filter is applied before other dimension filters and before FIXED LOD calculations, creating a smaller dataset the rest of the view works on. Use it when a Top N filter should operate within a selected category, for example top 10 products within Electronics, or when a FIXED LOD must respect a filter. Add a filter to context from its right-click menu; it turns grey. Too many context filters can hurt performance.
36
What is the difference between a live connection and an extract in Tableau?
Show answer
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.
37
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.
38
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.
39
Write SQL to find the median order value overall and per city.
Show answer
-- Works in PostgreSQL, Snowflake, SQL Server (with OVER for SQL Server)
SELECT
city,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY order_value) AS median_value,
AVG(order_value) AS mean_value
FROM orders
GROUP BY city
ORDER BY median_value DESC;
40
Many reporting queries in your team use SELECT DISTINCT to remove duplicate rows. What is the risk?
Show answer
SELECT DISTINCT often hides a real problem, such as a join on the wrong key or a one-to-many join that multiplies rows. The output looks clean, but sums computed before the DISTINCT, or legitimate repeated rows, can be wrong, and DISTINCT adds a costly sort on large tables. Instead, find why duplicates appear, fix the join or aggregate the child table first, and add a uniqueness test on the key.
Tips to crack the Protiviti Data Analytics interview
- Learn the basics of internal audit, risk management and controls, along with frameworks such as COSO for advisory interviews.
- For technology consulting roles, revise cybersecurity basics, SQL and data visualisation, and mention any relevant certifications you are pursuing.
- Practise explaining technical ideas in simple business language, since consultants often present findings to non-technical client stakeholders.
- 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; Protiviti interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Protiviti Data Analytics interview?
Candidates usually go through 4 stages: Resume screening or online assessment; 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 Protiviti Data Analytics interview?
This practice set leans on SQL (8), Tableau (5), Python for Analysis (4), Web & Marketing Analytics (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Protiviti interviewers ask you to walk through real work.
Are these the exact questions asked at Protiviti?
No. These are curated practice questions that match the skills and difficulty usually tested in consulting interviews like Protiviti's. They are not a leaked or verified list of past questions.
Where does Protiviti hire Data Analytics professionals in India?
Protiviti has large teams in Gurugram, Mumbai, Bengaluru, Hyderabad. Openings and locations change often, so check the Protiviti careers page and filter by city and experience.
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