01
How do you handle a dataset with 30% missing values in a key column?
Show answer
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.
Broadcom Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Broadcom hiring for Data Analytics roles
Broadcom is a global technology company that designs semiconductors for networking, broadband, wireless and storage, and also sells infrastructure software. Its software business includes VMware, CA Technologies and Symantec enterprise products acquired over the years. In India it hires chip design and verification engineers, embedded engineers and software engineers for its semiconductor and software divisions.
For Data Analytics roles, this Broadcom practice set focuses most on Python for Analysis (7), Data Cleaning (6), SQL (6), Excel (4). It has 2 easy, 29 medium, 9 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Hardware & semiconductors
- Headquarters
- Palo Alto, California, USA
- Main India offices
- Bengaluru, Pune, Chennai
Broadcom Data Analytics interview process
- Online assessment or resume screening
- Technical round 1 (digital design or coding)
- Technical round 2 (domain depth)
- Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).
Show answer
-- 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
03
What is referential integrity and how do orphan records appear in analytics data?
Show answer
Referential integrity means every foreign key value points to an existing row in the parent table, for example every order's customer_id exists in customers. Orphan records break this rule. They appear when source systems load tables at different times, records are deleted without cascading, or IDs are formatted differently across systems. Detect them with a LEFT JOIN where the parent key is NULL, and decide whether to fix, map or flag them.
04
Tell me about a time you worked with a marketing team to improve a campaign using data.
Show answer
Situation: a lead generation campaign had a high cost per lead and sales said leads were poor. Task: find what to change. Action: I joined ad data with CRM outcomes and found two audience segments produced most leads but almost no closed deals, while one smaller segment converted well. Result: we moved budget to that segment and changed the form questions; cost per qualified lead fell about 30% in six weeks.
05
How would you design an A/B test for a new checkout button?
Show answer
Define one primary metric, such as checkout conversion, plus guardrails like refund rate. State the hypothesis and minimum detectable effect, then calculate sample size and duration for chosen power and significance. Randomise users, not sessions, into control and variant, check the split is balanced, and run for full weeks to cover weekday patterns. Do not peek and stop early. Finally analyse with a proportion test and check key segments.
06
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.
07
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.
08
What is a composite model in Power BI?
Show answer
A composite model mixes storage modes in one dataset: some tables in Import, some in DirectQuery, and some in Dual. For example, a huge transaction table stays in DirectQuery for freshness while small dimension tables are imported for speed. Aggregation tables can be imported so most visuals hit memory, with DirectQuery used only for detailed drill-down. The trade-off is more complexity and relationship rules to manage.
09
Difference between a CTE, a subquery and a temp table?
Show answer
A subquery is inline; a CTE is a named, readable block for one statement; a temp table is physically stored for the session and can be indexed and reused.
10
Write a Python function that reads all Excel files in a folder and combines them into one DataFrame.
Show answer
from pathlib import Path
import pandas as pd
def combine_excel(folder):
frames = []
for f in Path(folder).glob('*.xlsx'):
df = pd.read_excel(f)
df['source_file'] = f.name
frames.append(df)
return pd.concat(frames, ignore_index=True)
all_sales = combine_excel('monthly_reports')
11
What are pre-attentive attributes and how do you use them in charts?
Show answer
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.
12
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.
13
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.
14
Average delivery time improved by 4 minutes, but customer complaints about late deliveries went up. How do you investigate?
Show answer
An average can improve while the tail gets worse. I would look at the distribution: median, 90th and 95th percentiles and the share of orders beyond the promised time. Then break it down by city, time slot and restaurant to see where late orders cluster. Also check whether promised times changed, whether complaint tracking changed and whether a few very fast orders pulled the mean down.
15
Your pandas script takes 40 minutes on 10 million rows. How do you speed it up?
Show answer
First profile to find the slow step instead of guessing. Common fixes: replace row-wise apply and loops with vectorised operations, convert repeated strings to category dtype, downcast numeric types, read only needed columns with usecols, and filter early. Push heavy joins and aggregations into SQL. If it is still slow, use a columnar engine such as DuckDB or Polars, or store intermediate data as Parquet instead of CSV.
16
A junior analyst wants to load one huge flat table with every column into Power BI instead of a star schema. How do you advise them?
Show answer
A flat table is quick to start and fine for a small one-off analysis. But at scale it bloats the model because repeated text attributes compress poorly, makes DAX filters and time intelligence harder, and breaks when you add a second fact like targets or returns. I would show them a star schema with one fact and a few dimensions, compare model size and measure speed, and let the results convince them.
17
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)
18
Explain INDEX and MATCH together.
Show answer
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.
19
How do you merge two DataFrames on different column names?
Show answer
pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.
20
How do you connect Python to a SQL database?
Show answer
Create an engine with SQLAlchemy or a driver such as psycopg2, then run pd.read_sql(query, engine) to load results into a DataFrame.
21
Write SQL to find the top 3 products by revenue within each category.
Show answer
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;
22
What is a conformed dimension?
Show answer
A conformed dimension is one dimension table with the same keys, attributes and meanings shared by several fact tables. For example, one date and one product dimension used by sales, returns and inventory facts. Because they conform, you can compare sales and returns by the same product category in one report without mismatched definitions. Conformed dimensions are the main way an enterprise warehouse stays consistent across departments.
23
Write SQL to show each category's percentage share of total revenue.
Show answer
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
24
What are statistical power and minimum detectable effect (MDE)?
Show answer
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.
25
Write an Excel formula to look up a price using two criteria, product and city.
Show answer
=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
26
Difference between UNION and UNION ALL?
Show answer
UNION removes duplicate rows and sorts internally; UNION ALL keeps every row and is faster.
27
What do the match_mode and search_mode arguments of XLOOKUP control?
Show answer
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.
28
Your sales data contains negative quantities. What do you do with them?
Show answer
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.
29
What is cohort analysis?
Show answer
Grouping users by a shared start date or trait and tracking behaviour over time, for example monthly retention per signup month.
30
You loaded a month of sales into the warehouse. How do you check the load is complete and correct?
Show answer
Reconcile against the source: compare row counts, total amount, and counts by day and by store between source and target. Check for duplicate keys, nulls in mandatory columns, and dates outside the expected month. Compare with the previous month to spot big unexplained jumps. Spot-check a few individual transactions end to end. Automate these checks so each future load produces a small pass or fail report.
31
What is a junk dimension?
Show answer
A junk dimension combines several low-cardinality flags and indicators, such as payment type, is_gift, is_express and order channel, into one small dimension table. Each row is a unique combination of those values, and the fact table stores a single key to it. This keeps the fact table narrow and avoids many tiny dimensions or loose flag columns. It works best when the combinations are limited, typically a few hundred rows.
32
A retail chain asks you which of its 120 stores it should close. How do you approach it?
Show answer
Clarify the goal first: profit, cash or market share. Build a store-level dataset with revenue, contribution margin after rent and staff, trend over 24 months, footfall, catchment demographics and cannibalisation with nearby stores. Separate structural losers from stores that are new or temporarily disrupted. Estimate how much revenue would shift to nearby stores or online if a store closes. Present a shortlist with scenarios and risks, not a single answer.
33
Write Python code to fit a simple linear regression of sales on ad spend.
Show answer
import pandas as pd
import statsmodels.formula.api as smf
df = pd.read_csv('marketing.csv')
model = smf.ols('sales ~ ad_spend', data=df).fit()
print(model.summary()) # coefficients, p-values, R-squared
# predicted sales for a spend of 50,000
print(model.predict(pd.DataFrame({'ad_spend': [50000]})))
34
How should you document data cleaning decisions?
Show answer
Keep a cleaning log or README listing each rule, why it exists and its impact, for example removed 1,240 test orders flagged by email domain. Code-based cleaning in SQL, Python or Power Query steps is itself documentation if steps are named clearly. Record counts before and after each major step. This lets reviewers trust the numbers, helps a colleague rerun the work, and answers the question why your total differs from the raw file.
35
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;
36
How do you pivot rows into columns in SQL, for example sales by quarter as columns?
Show answer
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
37
Write SQL using LAG to compare each month's revenue with the previous month.
Show answer
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month,
SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 1) AS mom_pct
FROM monthly
ORDER BY month;
38
What are UTM parameters and why do they matter?
Show answer
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.
39
The customer table has near-duplicates like 'Rahul Sharma, 98xxxx1234' and 'Rahul Sharma, +91 98xxxx1234'. How do you deduplicate?
Show answer
First normalise: trim and lower-case names, collapse spaces, and strip country codes and symbols from phones and emails. Then match on strong keys such as phone or email. For remaining records use fuzzy matching on names with a similarity score, restricted to the same city or pin code to limit comparisons. Review borderline scores manually, keep a mapping from old IDs to a master ID, and fix entry at the source.
40
What is Row-Level Security?
Show answer
Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.
Tips to crack the Broadcom Data Analytics interview
- For hardware roles, revise digital electronics, Verilog, computer architecture and verification concepts such as SystemVerilog and UVM.
- For software roles, revise C, data structures, operating systems and networking, especially for VMware and infrastructure software teams.
- Be ready to go deep into your project, explaining design decisions and debugging steps, as interviewers often probe details.
- 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; Broadcom interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Broadcom Data Analytics interview?
Candidates usually go through 4 stages: Online assessment or resume screening; Technical round 1 (digital design or coding); Technical round 2 (domain depth); Hiring manager 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 Broadcom Data Analytics interview?
This practice set leans on Python for Analysis (7), Data Cleaning (6), SQL (6), Excel (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Broadcom interviewers ask you to walk through real work.
Are these the exact questions asked at Broadcom?
No. These are curated practice questions that match the skills and difficulty usually tested in hardware & semiconductors interviews like Broadcom's. They are not a leaked or verified list of past questions.
Where does Broadcom hire Data Analytics professionals in India?
Broadcom has large teams in Bengaluru, Pune, Chennai. Openings and locations change often, so check the Broadcom careers page and filter by city and experience.
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