Broadcom Data Science 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 Science 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 Science roles, this Broadcom practice set focuses most on Statistics and Probability (7), Python for Data Science (6), Deep Learning and NLP (5), SQL for Data Science (4). It has 5 easy, 29 medium, 6 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 Science 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.
Solved for BroadcomStart practicing
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Guardrails are checks around the model that keep inputs and outputs safe and on-topic. Input guardrails detect prompt injection, abuse or off-topic requests. Output guardrails check for personal data, toxic content, policy violations or invalid formats before the answer reaches the user. They can be rules, classifiers or a second LLM call. Good guardrails also define fallback responses and log blocked cases for review.
02
Pandas: compute a 7-day rolling average of daily sales for each store.
Term Frequency × Inverse Document Frequency: a word scores high if it is frequent in one document but rare across all documents. A classic way to turn text into numeric features.
04
Write Pandas code to read a large CSV in chunks and compute total sales per city.
# process 1 million rows at a time
import pandas as pd
totals = {}
for chunk in pd.read_csv('sales.csv', usecols=['city', 'amount'], chunksize=1_000_000):
part = chunk.groupby('city')['amount'].sum()
for city, amt in part.items():
totals[city] = totals.get(city, 0) + amt
result = pd.Series(totals).sort_values(ascending=False)
05
What is the difference between a subquery and a correlated subquery?
A normal subquery runs once on its own and its result is used by the outer query, for example WHERE price > (SELECT AVG(price) FROM products). A correlated subquery refers to columns of the outer query, so it is logically evaluated once per outer row, for example comparing each product with its own category average. Correlated subqueries can be slow; joins or window functions are usually faster alternatives.
06
The average delivery time is 30 minutes, yet many customers complain about late orders. How do you investigate?
An average hides the shape of the distribution. Delivery times are usually right-skewed, so a 30-minute mean can include many orders taking 60 minutes or more. Look at the median, the 90th and 95th percentiles and a histogram, then break them down by city, time of day and restaurant. Report the share of orders beyond the promised time, because that is what customers actually experience.
07
What is the difference between hard voting and soft voting in ensembles?
In hard voting each model predicts a class and the majority wins. In soft voting each model gives class probabilities, they are averaged, and the class with the highest average wins. Soft voting usually works better because it uses each model's confidence, but it needs calibrated probabilities from all models. Hard voting is simpler and works even with models that only output labels.
08
You must forecast demand for a new product that has only three weeks of history. What do you do?
Three weeks is too little to learn seasonality, so borrow information. Find similar past launches by category, price band and channel, and use their early sales curves as analogues. A global model trained across many products, with product attributes as features, can also forecast a cold-start item. Combine this with planned marketing spend and business judgement, show a wide interval, and update the forecast weekly as real data arrives.
09
You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?
Target encoding replaces each category with the average target value for that category, for example the churn rate per city. It handles high-cardinality columns compactly. The main risk is leakage: if a row's own target is included in its encoding, the model sees the answer and overfits. Prevent it with out-of-fold encoding, smoothing toward the global mean for rare categories, and fitting only on training data.
10
Write Pandas code to cap outliers at the 1st and 99th percentile.
# fit limits on training data only, then apply to test
low, high = train['income'].quantile([0.01, 0.99])
train['income_capped'] = train['income'].clip(low, high)
test['income_capped'] = test['income'].clip(low, high)
It applies Bayes' theorem assuming features are independent given the class. The assumption is rarely true but the model is fast and works well for text.
12
Marketing wants to measure the impact of a TV and outdoor campaign that cannot be randomised at user level. How do you design the measurement?
Plan a geo experiment: select comparable cities or regions, randomly assign some to run the campaign and keep others as control, matching them on past sales trends. Measure the difference in sales during and after the campaign, using methods like difference-in-differences or synthetic control to build a counterfactual. Run a pre-period check that test and control regions track each other, and include enough regions for reasonable power.
The novelty effect is when users engage more with a change simply because it is new, so early results overstate the long-term lift. The primacy effect is the opposite: existing users resist change at first, so early results understate it. Both fade with time. To detect them, plot the treatment effect by day and by user tenure, compare new users with existing users, and run the test long enough for behaviour to settle.
15
Your LLM app takes 8 seconds to respond and users complain. What do you do?
Break down where the time goes: retrieval, model time to first token and generation length. Stream tokens so users see output immediately. Cut prompt and output length, run retrieval and other calls in parallel, and cache frequent answers. Use a smaller or faster model for simple requests, and avoid chains of several LLM calls where one would do. Set a latency budget per step and monitor p95, not just averages.
16
You are building defect detection for a factory line where only 1 in 1,000 images shows a defect. How do you approach it?
With so few defects, a plain classifier will learn to say no defect. Collect every defect image possible and oversample them or use class-weighted or focal loss. Consider anomaly detection trained only on good parts, which flags anything unusual. Use augmentation that mimics real variation in lighting and position. Evaluate with recall at a fixed false alarm rate, since missing a defect usually costs far more than a manual recheck.
17
Your test shows a 2 percent lift in conversion, but revenue per user went down. What do you do?
Do not ship on conversion alone. Check whether the change shifted buyers to cheaper items, increased discount usage or pulled in low-value orders. Break down revenue into conversion times average order value, look at item mix, and confirm the revenue drop is statistically meaningful rather than noise. Then go back to the agreed primary metric and guardrails; if revenue is the business goal, it should guide the decision.
18
What is chunking in RAG and how do you choose a chunk size?
Chunking splits documents into smaller pieces before embedding them, so retrieval returns focused passages that fit in the prompt. Small chunks, like 200 tokens, are precise but lose context; large chunks, like 1,000 tokens, keep context but add noise and cost. Split on natural boundaries such as headings and paragraphs, add some overlap, and keep metadata like title and section. Then tune chunk size using retrieval metrics on real questions.
19
What is normalisation, and why are analytics tables often denormalised?
Normalisation splits data into related tables to remove redundancy, so each fact is stored once, which suits transactional systems with many updates. Analytics workloads mostly read large volumes, and joins across many tables are slow and error-prone. So warehouses often use denormalised, wide tables or star schemas that repeat some attributes. The trade-off is more storage and the need to keep copies consistent through pipelines.
Covariance shows the direction of a relationship but depends on units. Correlation is covariance scaled to −1…+1, so it shows direction and strength and is unit-free.
21
How would you design an ML platform for a team of 20 data scientists?
I would start from the team's pain points, then provide shared building blocks: a standard project template, managed notebooks with access to the warehouse, experiment tracking and a model registry, an orchestrator like Airflow for pipelines, a feature store if real-time use cases exist, a standard serving path using containers, and default monitoring. Prefer managed services over custom builds, and add self-serve documentation so teams do not depend on one platform engineer.
22
Describe a time you had to push back on or prioritise many ad-hoc data requests.
Situation: as the only analyst for three teams, I received 15 ad-hoc SQL requests a week and my model work stalled. Task: protect focus without upsetting stakeholders. Action: I grouped requests, built a self-serve dashboard covering the 5 most common questions, and set up a simple weekly priority review with the managers. Result: ad-hoc requests dropped by 60 percent and the model shipped a month later as planned.
23
How would you optimise a slow Pandas pipeline on a 5 GB file?
Read in chunks or use usecols and dtypes, convert strings to category, avoid apply with row loops in favour of vectorised ops, and consider Polars, Dask or Parquet storage.
24
What is standard deviation and what does it tell you?
An OEC is a single agreed metric, or a weighted combination of metrics, that represents long-term business value and is used to judge experiments. Good OECs are sensitive enough to move within a test's duration yet aligned with long-term goals; for example, sessions per user with penalties for churn rather than raw clicks. Having an OEC prevents teams from each optimising their own metric and makes ship decisions consistent across the organisation.
26
Write Python code that validates prediction requests with Pydantic before passing them to a model.
WITH signups AS (
SELECT user_id, DATE_TRUNC('week', signup_date) AS cohort, signup_date
FROM users
), active AS (
SELECT DISTINCT s.user_id, s.cohort
FROM signups s
JOIN events e ON e.user_id = s.user_id
AND e.event_date >= s.signup_date + INTERVAL '7 day'
AND e.event_date < s.signup_date + INTERVAL '14 day'
)
SELECT s.cohort, COUNT(*) AS users,
ROUND(100.0 * COUNT(a.user_id) / COUNT(*), 1) AS week1_retention_pct
FROM signups s
LEFT JOIN active a ON a.user_id = s.user_id
GROUP BY s.cohort
ORDER BY s.cohort;
28
What is the difference between layer normalisation and batch normalisation?
Batch normalisation normalises each feature using the mean and variance across the batch, so it depends on batch size and behaves differently in training and inference. Layer normalisation normalises across the features of each single example, so it works with any batch size and with variable-length sequences. That is why transformers use layer norm, often in the pre-norm position, while CNNs traditionally use batch norm.
29
Your A/B test shows a significant lift after 2 days. The product team wants to ship now. What do you say?
I would ask them to wait for the planned duration. Checking early and stopping on the first significant result, called peeking, inflates false positives. Two days also misses weekly patterns and novelty effects, where users click a new feature just because it is new. If speed matters, we can use a sequential testing method designed for early stopping. Otherwise we run the full sample size calculated before launch.
Use the IQR rule (below Q1-1.5 IQR or above Q3+1.5 IQR), z-scores beyond 3, box plots or model-based methods such as Isolation Forest, then decide whether to cap, remove or keep them.
31
What is a t-test and when do you use a paired t-test?
A t-test compares means when the population variance is unknown. A two-sample t-test compares two independent groups, such as users in control and treatment. A paired t-test is used when the same units are measured twice, such as the same students before and after training. Pairing removes variation between individuals, so it is more powerful. Welch's t-test is preferred when the two groups have unequal variances.
32
A Pandas merge suddenly returns many more rows than the left table had. What happened?
The join key is probably not unique on the right side, so each left row matches several right rows and the result multiplies. Check with right['key'].duplicated().sum(). Other causes are null or blank keys matching each other after cleaning, or joining on the wrong column. Fix it by deduplicating or aggregating the right table first, and use merge(..., validate='many_to_one') so Pandas raises an error when the assumption breaks.
BERT is an encoder-only transformer that reads text in both directions at once. It was pretrained with masked language modelling, where about 15 percent of tokens are hidden and the model predicts them, plus next sentence prediction in the original version. This gives contextual embeddings, so a word's vector depends on its sentence. You fine-tune BERT with a small task head for classification, NER or question answering.
Transformers use self-attention to process all tokens in parallel and capture long-range context, making them faster to train and more scalable than sequential RNNs.
35
What are interaction features and when do they help?
Interaction features combine two or more variables, such as price times quantity, or income divided by number of dependents. They help linear models capture effects where one variable changes the impact of another. Tree models can learn some interactions themselves, but explicit ratios often still help. Too many automatic interactions explode the feature count and overfit, so build them from domain logic and validate each one.
36
Pandas: find the top 3 products by revenue in each region.
An ID has no real meaning, so high importance means the model is memorising individual customers. It often happens when the same customers appear in both train and test, or when IDs were assigned in time order and act as a proxy for date or signup cohort. I would drop the ID, use a group-based split by customer, and if timing matters, add an explicit feature like account age instead.
Bias occurs when the sample does not represent the population. Reduce it with random or stratified sampling, higher response rates and by checking sample demographics against the population.
39
Pandas: find customers whose monthly spend increased for three consecutive months.
It fits a line minimising squared error. Assumes linearity, independent errors, constant variance (homoscedasticity), normally distributed residuals and no strong multicollinearity.
Tips to crack the Broadcom Data Science 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 Science 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 Science interview?
This practice set leans on Statistics and Probability (7), Python for Data Science (6), Deep Learning and NLP (5), SQL for Data Science (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 Science 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.