Nokia Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Nokia hiring for Data Analytics roles
Nokia is a Finnish technology company that today focuses on telecom network infrastructure, including mobile networks, fixed broadband, optical and IP networks, and cloud network software. It also runs Nokia Bell Labs for research. In India it has research, development and manufacturing operations and hires software engineers, network engineers, testers and hardware engineers.
For Data Analytics roles, this Nokia practice set focuses most on SQL (7), Excel (6), Data Cleaning (5), Power BI (4). It has 12 easy, 26 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Company type
Telecom
Headquarters
Espoo, Finland
Main India offices
Bengaluru, Chennai, Noida, Gurugram
Nokia Data Analytics interview process
Online assessment (aptitude + technical)
Technical interview 1
Technical interview 2 or 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 transaction and what do the ACID properties mean?
A transaction is a group of SQL statements that succeed or fail together, such as debiting one account and crediting another. ACID: Atomicity means all or nothing; Consistency means rules and constraints stay valid; Isolation means concurrent transactions do not see each other's partial work; Durability means committed data survives crashes. Analysts mostly read data, but knowing ACID explains why reports should read committed data.
02
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
03
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.
04
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.
05
What is regression to the mean? Give a business example.
Regression to the mean means extreme results tend to be followed by more average results, simply because part of the extreme was luck. For example, if you pick the 10 worst-performing stores this quarter and give them a training programme, many will improve next quarter even without training. To claim the programme worked, compare them with similar low performers who did not get it.
06
What is an action title and why should you use one?
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.
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.
08
How do you handle a dataset with 30% missing values in a key column?
Understand why it is missing, consider imputation or a missing flag, test sensitivity, and talk to data owners about fixing collection if it is systematic.
A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).
The Data Model is an in-memory columnar store inside Excel that holds several related tables, and Power Pivot is the interface to manage it. Instead of flattening data with VLOOKUPs, you load fact and dimension tables, create relationships and write DAX measures. It handles millions of rows that would not fit on a sheet, and PivotTables built on it can slice across tables. It is the same engine idea that Power BI uses.
11
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.
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.
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.
14
What is the difference between a dashboard and a report?
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.
18
What do the match_mode and search_mode arguments of XLOOKUP control?
match_mode decides how a value is matched: 0 exact (default), -1 exact or next smaller, 1 exact or next larger, and 2 wildcard. search_mode decides direction: 1 searches first to last, -1 searches last to first, and 2 or -2 use binary search on sorted data. Searching last to first is a quick way to fetch the latest entry for a customer without sorting, and next-smaller matching handles tax slabs or commission bands.
An A/A test splits users into two groups that both see the same version. Since nothing differs, you expect no significant difference except about 5% of the time by chance. It checks that randomisation, tracking and the analysis pipeline work correctly, and it reveals sample ratio mismatches or metric variance before real tests depend on the platform. Running many A/A tests also confirms your false positive rate is near 5%.
21
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
22
How do you combine text from several cells with a separator in Excel?
-- join first and last name with a space
=TEXTJOIN(" ", TRUE, A2, B2)
-- all products of one order in a single cell, comma separated
=TEXTJOIN(", ", TRUE, FILTER(C2:C500, A2:A500=F2))
-- older method
=A2 & " " & B2
-- TRUE in TEXTJOIN skips empty cells, which CONCAT and & do not
-- A2 = 'Priya Ramesh Iyer'
-- first name
=TEXTBEFORE(TRIM(A2), " ")
-- last name (text after the last space)
=TEXTAFTER(TRIM(A2), " ", -1)
-- older Excel: first name
=LEFT(TRIM(A2), FIND(" ", TRIM(A2)&" ")-1)
-- or use Data > Text to Columns or Flash Fill (Ctrl+E) for a one-off task
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.
This is an encoding mismatch: a UTF-8 file was read as a different encoding such as Latin-1 or Windows-1252. Re-read it with the right encoding, for example pd.read_csv(path, encoding='utf-8') or 'utf-8-sig' for files with a byte order mark, or set the file origin in Excel and Power Query import. Avoid re-saving through tools that change encoding, and agree on UTF-8 for all exchanges, especially for Hindi or other Indian language text.
CREATE TABLE fact_sales (
date_key INT NOT NULL REFERENCES dim_date(date_key),
product_key INT NOT NULL REFERENCES dim_product(product_key),
store_key INT NOT NULL REFERENCES dim_store(store_key),
invoice_no VARCHAR(20) NOT NULL, -- degenerate dimension
quantity INT NOT NULL,
net_amount DECIMAL(12,2) NOT NULL,
discount_amount DECIMAL(12,2) DEFAULT 0
);
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.
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;
32
Your sales data contains negative quantities. What do you do with them?
Do not delete them blindly. First find out what they mean: often they are returns, cancellations or credit notes, which are valid business events. Check with the source team or a documentation note. If they are returns, keep them but report gross sales, returns and net sales separately. If some are clearly entry errors, such as a negative quantity on a new order, flag and correct them and log the rule.
33
Write SQL to find the top 3 products by revenue within each category.
WITH ranked AS (
SELECT category,
product,
SUM(amount) AS revenue,
DENSE_RANK() OVER (
PARTITION BY category
ORDER BY SUM(amount) DESC
) AS rnk
FROM sales
GROUP BY category, product
)
SELECT category, product, revenue
FROM ranked
WHERE rnk <= 3
ORDER BY category, rnk;
ROW_NUMBER gives a unique sequence. RANK gives ties the same rank and skips the next (1, 1, 3). DENSE_RANK gives ties the same rank without gaps (1, 1, 2).
35
How do you pivot rows into columns in SQL, for example sales by quarter as columns?
SELECT region,
SUM(CASE WHEN quarter = 'Q1' THEN amount ELSE 0 END) AS q1,
SUM(CASE WHEN quarter = 'Q2' THEN amount ELSE 0 END) AS q2,
SUM(CASE WHEN quarter = 'Q3' THEN amount ELSE 0 END) AS q3,
SUM(CASE WHEN quarter = 'Q4' THEN amount ELSE 0 END) AS q4
FROM sales
GROUP BY region;
-- conditional aggregation works in every database; some also offer PIVOT
36
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.
-- C2 = invoice date, TODAY() = as-of date
=LET(age, TODAY()-C2,
IFS(age<=30, "0-30",
age<=60, "31-60",
age<=90, "61-90",
TRUE, "90+"))
-- then a PivotTable with bucket in rows and SUM of amount gives the ageing report
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.
40
How do you make a dashboard accessible, for example for colour-blind users?
Avoid red-green as the only difference; use colour-blind-safe palettes such as blue and orange, and add icons, labels or patterns so meaning never depends on colour alone. Ensure enough contrast between text and background, use readable font sizes, and add alt text to visuals. Keep a logical tab order for keyboard users, and offer a table view of chart data. Accessible dashboards are usually clearer for everyone.
Tips to crack the Nokia Data Analytics interview
Revise networking fundamentals including OSI layers, routing, switching and IP addressing, which are core to many Nokia roles.
Practise C, C++ or Python programming and Linux basics, since telecom software often runs on Linux platforms.
Read about Nokia's shift from phones to network infrastructure and its 5G work to answer company questions accurately.
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; Nokia interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Nokia Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); Technical interview 1; Technical interview 2 or 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 Nokia Data Analytics interview?
This practice set leans on SQL (7), Excel (6), Data Cleaning (5), Power BI (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Nokia interviewers ask you to walk through real work.
Are these the exact questions asked at Nokia?
No. These are curated practice questions that match the skills and difficulty usually tested in telecom interviews like Nokia's. They are not a leaked or verified list of past questions.
Where does Nokia hire Data Analytics professionals in India?
Nokia has large teams in Bengaluru, Chennai, Noida, Gurugram. Openings and locations change often, so check the Nokia careers page and filter by city and experience.