Cyient Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Cyient hiring for Data Analytics roles
Cyient is an Indian engineering and technology solutions company that started in geospatial services and now covers aerospace, rail, communications, medical and utilities engineering. It provides product design, embedded software, network engineering and data services to global manufacturers and operators. In India it hires freshers and experienced engineers for design, software, testing, GIS and analytics roles at its delivery centres.
For Data Analytics roles, this Cyient practice set focuses most on Dashboards and KPIs (7), Tableau (5), Statistics and Case Studies (5), Excel (4). It has 14 easy, 24 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Hyderabad, India
Main India offices
Hyderabad, Bengaluru, Noida
Cyient 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 the difference between extract filters and data source filters in Tableau?
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.
02
What is the difference between a table and a normal range?
-- A2:D500 holds Order, Customer, Region, Amount
=FILTER(A2:D500, C2:C500="North", "No orders")
-- two conditions (AND) multiply the tests
=FILTER(A2:D500, (C2:C500="North")*(D2:D500>10000), "No orders")
-- OR conditions add the tests
=FILTER(A2:D500, (C2:C500="North")+(C2:C500="East"))
04
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.
05
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;
06
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.
07
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.
Method chaining writes a sequence of transformations as one expression, each step returning a new DataFrame, for example df.query('sales > 0').assign(margin=lambda x: x.profit / x.sales).groupby('region').agg(total=('sales', 'sum')). It avoids many temporary variables, reads top to bottom like a recipe, and reduces SettingWithCopy issues. Wrap the chain in brackets with one step per line, and use pipe for custom functions.
Tableau is a visual analytics tool for building interactive charts and dashboards by dragging fields onto shelves. Tableau Desktop is used to author workbooks, Tableau Prep cleans and shapes data, Tableau Server and Tableau Cloud host and share dashboards with security and scheduled refresh, and Tableau Public is a free version where work is saved publicly. Analysts usually build in Desktop and publish to Server or Cloud.
Table calculations are computed on the aggregated results already in the view, not on the underlying rows. Examples are running total, percent of total, difference from previous, rank and moving average. Their result depends on the direction and partition you set with Compute Using, for example table across or by specific dimensions. They are quick to add from Quick Table Calculation, but incorrect Compute Using settings are a frequent cause of wrong numbers.
14
What is bidirectional cross-filtering in Power BI and when is it risky?
Normally filters flow from the one side to the many side, for example from Product to Sales. Bidirectional filtering lets filters also flow back, so a Sales filter can limit Product. It helps in some many-to-many or bridge-table cases. The risks are ambiguous filter paths, unexpected results and slower performance. Prefer single direction and use CROSSFILTER inside a specific measure when you need both directions only there.
15
What is the difference between mean and median for reporting salaries?
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.
17
A stakeholder wants 20 more visuals added to an already crowded dashboard page. How do you respond?
I would ask which decisions each new visual supports and how often they will look at it. Usually a few are essential and the rest are nice to have. I would propose keeping the main page focused on core KPIs and moving detail into drill-through pages, tooltips or a separate analysis tab. Showing a quick mock-up of both versions helps them see that the cleaner page answers questions faster.
18
What is a snowflake schema and when would you choose it over a star schema?
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.
19
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.
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.
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.
22
How can a chart mislead even when the numbers are correct?
Common tricks are a truncated y-axis on bar charts, which exaggerates small differences; inconsistent scales across side-by-side charts; dual axes chosen to imply a correlation; cherry-picked time windows; cumulative charts that always rise; and area or 3D effects that distort size. Bars should start at zero, while line charts may zoom in if clearly labelled. Always check that the visual impression matches the actual magnitude.
23
What is RAG status and how should thresholds be set?
RAG means red, amber, green, a traffic-light status that shows at a glance whether a KPI is on track. Thresholds should be agreed with the business owner in advance, for example green at or above 100 percent of target, amber from 90 to 99 percent, red below 90 percent. Do not rely only on colour; add icons or text for colour-blind users, and avoid so many reds that nobody reacts.
24
Which KPIs would you include in an HR attrition dashboard?
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.
25
What does apply do in pandas and why can it be slow?
apply runs a Python function on each row or column of a DataFrame, or each value of a Series. It is flexible but slow on large data because it loops in Python rather than using optimised C code. For example, df['total'] = df['qty'] * df['price'] is far faster than an apply with a lambda. Prefer vectorised operations, np.where, np.select, map with a dictionary, or string accessor methods first.
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
How would you investigate a sudden 20% drop in daily sales?
Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.
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.
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.
SELECT customer_id,
CASE WHEN email IS NULL OR email NOT LIKE '%_@_%._%' THEN 1 ELSE 0 END AS bad_email,
CASE WHEN LENGTH(pincode) <> 6 THEN 1 ELSE 0 END AS bad_pincode,
CASE WHEN signup_date > CURRENT_DATE THEN 1 ELSE 0 END AS future_signup,
CASE WHEN age NOT BETWEEN 18 AND 100 THEN 1 ELSE 0 END AS bad_age
FROM customers;
-- sum each flag to report the error rate per rule
32
Write SQL to calculate monthly customer churn rate.
WITH active AS (
SELECT DISTINCT customer_id,
DATE_TRUNC('month', order_date) AS month
FROM orders
)
SELECT a.month,
COUNT(*) AS active_customers,
COUNT(*) - COUNT(n.customer_id) AS churned_next_month,
ROUND(100.0 * (COUNT(*) - COUNT(n.customer_id)) / COUNT(*), 1) AS churn_pct
FROM active a
LEFT JOIN active n
ON n.customer_id = a.customer_id
AND n.month = a.month + INTERVAL '1 month'
GROUP BY a.month
ORDER BY a.month;
33
What is the LET function in Excel and why would you use it?
LET assigns names to intermediate results inside a single formula, for example =LET(sales, SUM(D2:D100), cost, SUM(E2:E100), (sales-cost)/sales). It makes long formulas readable and faster, because a repeated sub-calculation is evaluated once instead of several times. It is especially useful with dynamic arrays, where the same FILTER result might otherwise be recalculated three or four times in one nested formula.
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.
SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
37
Write SQL to calculate users, conversions and conversion rate for each test variant.
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;
38
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.
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)
Tips to crack the Cyient Data Analytics interview
Revise fundamentals of your engineering branch, since Cyient roles often mix domain engineering knowledge with software or data skills.
Practise basic programming, SQL queries and simple aptitude problems, which commonly feature in the first screening stage.
Learn about Cyient business units such as aerospace, rail and geospatial to explain which area interests you and why.
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; Cyient interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Cyient 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 Cyient Data Analytics interview?
This practice set leans on Dashboards and KPIs (7), Tableau (5), Statistics and Case Studies (5), Excel (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Cyient interviewers ask you to walk through real work.
Are these the exact questions asked at Cyient?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Cyient's. They are not a leaked or verified list of past questions.
Where does Cyient hire Data Analytics professionals in India?
Cyient has large teams in Hyderabad, Bengaluru, Noida. Openings and locations change often, so check the Cyient careers page and filter by city and experience.