Texas Instruments Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Texas Instruments hiring for Data Analytics roles
Texas Instruments is an American semiconductor company that designs and manufactures analog chips and embedded processors used in industrial, automotive, personal electronics and communications products. Its Bengaluru centre, set up in 1985, was one of the first multinational R&D centres in India. It hires analog, digital, verification, embedded software and applications engineers.
For Data Analytics roles, this Texas Instruments practice set focuses most on SQL (6), Python for Analysis (5), Power BI (5), Web & Marketing Analytics (5). It has 2 easy, 31 medium, 7 hard questions, so you can start easy and work up to the harder ones.
Company type
Hardware & semiconductors
Headquarters
Dallas, USA
Main India offices
Bengaluru
Texas Instruments Data Analytics interview process
Online assessment (aptitude + electronics)
Technical interview 1
Technical interview 2
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
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 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.
Find out which system or region produced each format, since a source column often explains it. Values with a day above 12 reveal the format unambiguously. Parse with an explicit format per source, for example pd.to_datetime(col, format='%d/%m/%Y', errors='coerce'), rather than letting the tool guess. Check failed or suspicious parses, such as future dates, and ask the source team to send ISO format yyyy-mm-dd going forward.
06
Write SQL to group raw page events into sessions using a 30-minute inactivity rule.
WITH ordered AS (
SELECT user_id, event_ts,
LAG(event_ts) OVER (PARTITION BY user_id ORDER BY event_ts) AS prev_ts
FROM page_events
),
flagged AS (
SELECT *,
CASE WHEN prev_ts IS NULL
OR event_ts - prev_ts > INTERVAL '30 minutes'
THEN 1 ELSE 0 END AS new_session
FROM ordered
)
SELECT user_id, event_ts,
SUM(new_session) OVER (PARTITION BY user_id ORDER BY event_ts) AS session_no
FROM flagged;
07
Marketing wants to A/B test two email subject lines on a list of 2,000 subscribers. What do you advise?
With 1,000 per group and a typical click rate of 3%, only a very large difference, roughly 70% relative or more, would be detectable, so a small real lift would look like noise. I would suggest testing on a larger send, pooling several campaigns, or using open-to-click rate with a bigger effect. If they still run it, report the confidence interval and treat the result as directional.
08
What is the medallion architecture (bronze, silver, gold)?
Medallion architecture organises a lakehouse into three layers. Bronze holds raw data exactly as ingested, for audit and reprocessing. Silver holds cleaned, deduplicated, typed and joined data. Gold holds business-ready tables such as star schemas and aggregates used by dashboards. Each layer is built from the previous one by repeatable jobs, so if a cleaning rule changes you can rebuild silver and gold from bronze.
09
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.
10
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.
11
Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?
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.
12
Write a Python function that reads all Excel files in a folder and combines them into one DataFrame.
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
14
How do you handle null values in Tableau calculations?
// treat missing sales as zero
ZN(SUM([Sales]))
// replace a missing value with a default
IFNULL([Region], 'Unknown')
// test explicitly
IF ISNULL([Discount]) THEN 0 ELSE [Discount] END
// right-click a null indicator on an axis to filter or show nulls at default position
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.
17
Tell me about a time you had to say no to a stakeholder's request.
Situation: a regional manager asked me to show his team's numbers excluding two weeks of a stock-out. Task: be helpful without misreporting. Action: I explained that removing the period would make his region incomparable with others, and offered an annotation and a separate view showing the stock-out impact. Result: he accepted, the leadership review understood the dip, and the main dashboard stayed consistent for every region.
18
Brand keyword ads show a ROAS of 15, and the team wants to double the budget. How do you respond?
Brand search ROAS is usually inflated because people searching the brand name were already looking for you and would often click the free organic result anyway. Before increasing spend, I would propose an incrementality test, such as pausing brand ads in some matched cities or time periods and measuring the drop in total brand-search orders. The incremental ROAS is often far lower, so the extra budget may work better elsewhere.
19
A marketing campaign shows more sales. How do you prove it worked?
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
22
Write an Excel formula to look up a price using two criteria, product and city.
=XLOOKUP(1, (A2:A500=G2) * (B2:B500=H2), C2:C500, "Not found")
-- A = product, B = city, C = price
-- G2 and H2 hold the product and city to find
-- each condition returns TRUE/FALSE; multiplying gives 1 only where both match
23
Write a Tableau table calculation for year-over-year sales growth.
// with YEAR(Order Date) in the view, Compute Using: Table (across)
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1))
/ ABS(LOOKUP(ZN(SUM([Sales])), -1))
// the built-in Quick Table Calculation 'Year over Year Growth' generates similar logic
// format the field as a percentage
24
How do you merge two DataFrames on different column names?
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.
28
Tell me about a time you designed or improved a data model.
Situation: our sales reports used five separate Excel extracts and numbers never matched. Task: build one reliable model. Action: I listed the business questions, fixed the grain at invoice line, built conformed date, product and branch dimensions and moved logic into SQL views, then rebuilt the Power BI reports on top. Result: refresh time dropped from 40 to 6 minutes and month-end reconciliation queries fell sharply.
29
Your workbook has 200,000 rows and hundreds of VLOOKUPs and takes minutes to recalculate. What do you do?
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.
30
Write a DAX measure that counts new customers, meaning customers who bought in the selected period but never before it.
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.
32
Write pandas code to calculate customer acquisition cost and payback period in months for each channel.
SELECT category,
SUM(amount) AS revenue,
ROUND(100.0 * SUM(amount)
/ SUM(SUM(amount)) OVER (), 2) AS pct_share
FROM sales
GROUP BY category
ORDER BY revenue DESC;
-- SUM(SUM(amount)) OVER () is the grand total across all groups
34
How do you handle large CSV files that do not fit in memory in Python?
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.
36
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.
37
After a website migration, the share of direct traffic jumped from 15% to 45%. What could cause it?
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.
38
Write SQL to find users who logged in on at least 3 consecutive days.
WITH d AS (
SELECT DISTINCT user_id, CAST(login_time AS DATE) AS dt
FROM logins
),
g AS (
SELECT user_id, dt,
dt - CAST(ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY dt) AS INT) AS grp
FROM d
)
SELECT DISTINCT user_id
FROM g
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;
-- gaps and islands: consecutive dates share the same grp value
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.
40
A manager asks for a dashboard that refreshes every minute. How do you decide the right refresh frequency?
Match refresh to how quickly decisions are made. A monthly board pack needs daily refresh at most, while a warehouse dispatch board may need 15-minute or live updates. Faster refresh costs more in source load, licensing and complexity, and can show incomplete data. Ask how quickly someone will act on a change, check when upstream systems finish loading, and always display the last refreshed time on the dashboard.
Tips to crack the Texas Instruments Data Analytics interview
Revise analog electronics, op-amps, circuit analysis and semiconductor device basics, which are core topics for many TI roles.
For digital and embedded roles, revise digital design, microcontrollers and C programming with practical examples from lab work.
Be ready to solve circuit problems on paper and explain each step clearly, as interviewers often focus on fundamentals.
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; Texas Instruments interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Texas Instruments Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + electronics); Technical interview 1; Technical interview 2; 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 Texas Instruments Data Analytics interview?
This practice set leans on SQL (6), Python for Analysis (5), Power BI (5), Web & Marketing Analytics (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Texas Instruments interviewers ask you to walk through real work.
Are these the exact questions asked at Texas Instruments?
No. These are curated practice questions that match the skills and difficulty usually tested in hardware & semiconductors interviews like Texas Instruments's. They are not a leaked or verified list of past questions.
Where does Texas Instruments hire Data Analytics professionals in India?
Texas Instruments has large teams in Bengaluru. Openings and locations change often, so check the Texas Instruments careers page and filter by city and experience.