01
How does a CNN work?
Show answer
Convolution filters slide over the input to detect local patterns, pooling reduces size, and deeper layers combine edges into shapes and objects. Used mainly for images.
NVIDIA Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About NVIDIA hiring for Data Science roles
NVIDIA designs GPUs and accelerated computing platforms used in gaming, data centres, AI and automotive. It also develops software like CUDA and AI frameworks. Its India offices in Pune, Bengaluru and Hyderabad work on chip design, verification, system software and AI. It hires for software engineers, verification engineers and test engineers.
For Data Science roles, this NVIDIA practice set focuses most on Machine Learning (6), Deep Learning and NLP (5), Generative AI and LLMs (5), SQL for Data Science (5). It has 1 easy, 31 medium, 8 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Hardware & semiconductors
- Headquarters
- Santa Clara, California, USA
- Main India offices
- Pune, Bengaluru, Hyderabad
NVIDIA Data Science interview process
- Online assessment or technical screening
- Technical round 1
- Technical round 2
- Technical round 3 or manager round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
How does the Gini impurity differ from entropy?
Show answer
Both measure node impurity; Gini is cheaper to compute and entropy is slightly more sensitive to class balance, with similar trees in practice.
03
Write Python code to compare revenue per user between two groups using Welch's t-test.
Show answer
import numpy as np
from scipy import stats
ctrl = df.loc[df.variant == 'control', 'revenue'].to_numpy()
treat = df.loc[df.variant == 'treatment', 'revenue'].to_numpy()
t, p = stats.ttest_ind(treat, ctrl, equal_var=False) # Welch
diff = treat.mean() - ctrl.mean()
se = np.sqrt(treat.var(ddof=1) / len(treat) + ctrl.var(ddof=1) / len(ctrl))
print(f'diff={diff:.2f} 95% CI=({diff - 1.96*se:.2f}, {diff + 1.96*se:.2f}) p={p:.4f}')
04
Write a Python function to split a long text into overlapping chunks by word count for a RAG index.
Show answer
def chunk_words(text, size=200, overlap=40):
if overlap >= size:
raise ValueError('overlap must be smaller than size')
words = text.split()
chunks, step = [], size - overlap
for start in range(0, len(words), step):
chunks.append(' '.join(words[start:start + size]))
if start + size >= len(words):
break
return chunks
print(len(chunk_words(doc_text)))
05
Write SQL to compute a 7-day rolling average of daily active users.
Show answer
WITH daily AS (
SELECT activity_date, COUNT(DISTINCT user_id) AS dau
FROM events
GROUP BY activity_date
)
SELECT activity_date, dau,
AVG(dau) OVER (ORDER BY activity_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS dau_7d_avg
FROM daily
ORDER BY activity_date;
06
Random forest vs a single decision tree?
Show answer
A forest averages many trees trained on bootstrapped rows and random feature subsets, which cuts variance and overfitting at the cost of interpretability.
07
How do you get reliable JSON output from an LLM?
Show answer
Use the provider's structured output or JSON schema mode when available, which constrains the output to your schema. Otherwise, describe the exact schema in the prompt with an example, and use low temperature. Always parse and validate the response in code, for example with Pydantic, and on failure retry with the error message included. Keep schemas small and flat, and use enums for fields with fixed values.
08
A feature was launched to all users with no control group. How would you estimate its causal impact?
Show answer
Without randomisation I would use quasi-experimental methods. Difference-in-differences compares the change in a group that got the feature early with one that did not, if rollout was staggered. Interrupted time series or synthetic control builds a counterfactual from pre-launch trends or similar markets. Regression discontinuity works if eligibility had a cut-off. I would state the assumptions clearly and check them with placebo tests.
09
You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?
Show answer
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
Your dataset is highly imbalanced (2% positives). How do you handle it?
Show answer
Use stratified splits, class weights, resampling (SMOTE or undersampling) and threshold tuning, and judge the model with PR-AUC or F1 instead of accuracy.
11
What is transfer learning?
Show answer
Reusing a model pretrained on a large dataset and fine-tuning it on a smaller task-specific dataset, saving data, time and compute.
12
You assign values to a filtered DataFrame and Pandas shows a SettingWithCopyWarning. What is going on and how do you fix it?
Show answer
The warning appears when you assign values to something that may be a copy of a slice instead of the original DataFrame, for example df[df.age > 30]['flag'] = 1. The original may not change. Fix it by using a single .loc call, like df.loc[df.age > 30, 'flag'] = 1, or by taking an explicit .copy() when you really want a separate DataFrame. Copy-on-write in newer Pandas versions makes this behaviour more predictable.
13
Your sales forecast scores well in k-fold cross-validation but fails in production. What could be wrong with the validation?
Show answer
Random k-fold mixes future and past rows, so the model trains on data from after the period it predicts. That leaks information and inflates scores. Use time-based splits instead: train on an expanding or rolling window and validate on the next period, like TimeSeriesSplit in scikit-learn. Leave a gap between train and validation when features use lagged values, so the test reflects real forecasting conditions.
14
What is the difference between iterators and iterables in Python?
Show answer
An iterable can produce an iterator (it has __iter__); an iterator has __next__ and remembers its position. A for-loop calls iter() on the iterable and then next() repeatedly.
15
Your NLP classifier works well on English reviews but fails on Hinglish reviews. What do you do?
Show answer
First measure the gap with a labelled Hinglish test set. Hinglish mixes scripts and spellings, so tokenisers built for English split it badly. I would try a multilingual or Indic model such as mBERT, XLM-R or IndicBERT, normalise common spellings, and add labelled Hinglish data, possibly with transliteration. Evaluate per language segment and monitor the language mix in production, because it changes over time.
16
Your classifier scores 98 percent on validation, but Grad-CAM shows it focuses on a watermark rather than the object. What is happening?
Show answer
The model has learned a shortcut: the watermark correlates with the label in your dataset, perhaps because images of one class came from a single source. It will fail when that correlation breaks. Remove or crop the watermarks, rebalance sources across classes, and create a validation set from different sources. Keep using saliency maps and slice-based evaluation to catch other spurious cues such as backgrounds or text.
17
What is gradient descent and what does the learning rate do?
Show answer
An optimisation method that updates weights in the opposite direction of the loss gradient. The learning rate sets the step size: too high and it diverges, too low and it is slow. Variants include SGD, mini-batch and Adam.
18
What is a confidence interval?
Show answer
A range built from sample data that would contain the true parameter in a stated percentage (for example 95%) of repeated samples. It is not the probability that the parameter lies inside this one interval.
19
After joining orders with a promotions table, total revenue doubled. What went wrong?
Show answer
The join created duplicate order rows, because some orders match more than one promotion row, so each amount is counted several times. Check by comparing COUNT(*) with COUNT(DISTINCT order_id) after the join. Fix it by aggregating the promotions table to one row per order first, picking one promotion with ROW_NUMBER, or summing revenue before the join. Always check row counts after every join.
20
How do you choose a learning rate for a neural network?
Show answer
Start with a sensible default such as 1e-3 for Adam, run a learning-rate range test, and use schedulers or warm-up while watching the loss curve.
21
Write SQL to compute week-1 retention by signup week.
Show answer
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;
22
Write Python code to fit a Holt-Winters model and forecast the next 12 months.
Show answer
from statsmodels.tsa.holtwinters import ExponentialSmoothing
s = df.set_index('month')['sales'].asfreq('MS')
train, test = s[:-12], s[-12:]
model = ExponentialSmoothing(
train, trend='add', seasonal='mul',
seasonal_periods=12
).fit()
forecast = model.forecast(12)
mae = (forecast - test).abs().mean()
print(forecast.round(1), 'MAE:', round(mae, 1))
23
What is the Augmented Dickey-Fuller test?
Show answer
The ADF test checks whether a series has a unit root, which means it is non-stationary. The null hypothesis is that a unit root exists. A small p-value, say below 0.05, lets you reject the null and treat the series as stationary. It is often paired with the KPSS test, whose null is the opposite, stationarity. Using both gives a more reliable conclusion before choosing how much to difference.
24
Explain the Law of Large Numbers.
Show answer
As the number of independent trials grows, the sample average converges to the true expected value, which is why larger samples give more stable estimates.
25
What is the difference between a star schema and a snowflake schema?
Show answer
A star schema has a central fact table, like sales with amounts and foreign keys, surrounded by denormalised dimension tables such as product, customer and date. A snowflake schema normalises dimensions further, for example product links to a separate category table. Star schemas need fewer joins and are faster and simpler for analysts. Snowflake schemas save storage and reduce duplication but make queries more complex.
26
Pandas: compute a 7-day rolling average of daily sales for each store.
Show answer
# df has columns: store, date, sales
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values(['store', 'date'])
df['sales_7d_avg'] = (
df.groupby('store')['sales']
.transform(lambda s: s.rolling(7, min_periods=1).mean())
)
27
How does K-means clustering work and how do you choose k?
Show answer
It assigns points to the nearest centroid and updates centroids until stable. Choose k with the elbow method, silhouette score or business context.
28
What is mixed precision training?
Show answer
Mixed precision training uses 16-bit floats, float16 or bfloat16, for most operations while keeping a 32-bit master copy of the weights. It roughly halves memory use and speeds up training on modern GPUs with tensor cores. With float16, a loss scaler multiplies the loss to avoid gradients underflowing to zero. bfloat16 has a wider range and usually needs no scaling. Final accuracy is normally the same as full precision.
29
Write Python code to find the top 3 most similar documents to a query using cosine similarity on embeddings.
Show answer
import numpy as np
def top_k(query_vec, doc_vecs, k=3):
q = query_vec / np.linalg.norm(query_vec)
d = doc_vecs / np.linalg.norm(doc_vecs, axis=1, keepdims=True)
scores = d @ q
idx = np.argsort(-scores)[:k]
return list(zip(idx, scores[idx]))
# doc_vecs shape: (n_docs, dim), query_vec shape: (dim,)
30
How do you perform feature selection?
Show answer
Use filter methods (correlation, chi-square), wrapper methods (RFE), embedded methods (Lasso, tree importance) and domain knowledge, validating each choice with cross-validation.
31
Write a Python function that deterministically assigns users to experiment variants using hashing.
Show answer
import hashlib
def assign(user_id, experiment, variants=('control', 'treatment'), weights=(50, 50)):
key = f'{experiment}:{user_id}'.encode()
bucket = int(hashlib.sha256(key).hexdigest(), 16) % 100
cumulative = 0
for v, w in zip(variants, weights):
cumulative += w
if bucket < cumulative:
return v
# Same user + experiment always gets the same variant;
# salting with the experiment name keeps tests independent
32
Write NumPy code to compute the Dice coefficient between a predicted and a true binary mask.
Show answer
import numpy as np
def dice(pred, true, eps=1e-7):
pred = pred.astype(bool)
true = true.astype(bool)
inter = np.logical_and(pred, true).sum()
return (2 * inter + eps) / (pred.sum() + true.sum() + eps)
# Dice = 2|A and B| / (|A| + |B|); 1 means perfect overlap
33
How do you turn text columns into features for a tabular model?
Show answer
Simple options are length, word count and keyword flags. TF-IDF vectors capture important words and work well with linear models. For meaning, use sentence embeddings from a pretrained model and optionally reduce them with PCA. You can also train a separate text classifier and feed its predicted score as one feature. Fit any vocabulary or encoder on training data only to avoid leakage.
34
How does a multi-armed bandit differ from a classic A/B test?
Show answer
A classic A/B test splits traffic in fixed proportions until the end, then decides, which is best for learning and clean inference. A multi-armed bandit, such as Thompson sampling, shifts traffic towards better-performing variants as data arrives, reducing the cost of showing a weak variant. Bandits suit short-lived decisions like headlines or promotions. They make unbiased effect estimates and guardrail analysis harder, so they are poor for major product decisions.
35
What is a sample ratio mismatch in an experiment and what do you do about it?
Show answer
A sample ratio mismatch means the observed split between groups differs from the planned split, for example 52 to 48 instead of 50 to 50 with a large sample. A chi-square test on the counts confirms it. It usually signals a bug in assignment, redirects, bot filtering or logging, so the results cannot be trusted. Do not analyse the metric; find and fix the root cause and rerun the test.
36
Your LLM gives different summaries for the same document on different runs, but auditors need consistent output. What do you do?
Show answer
Set temperature to zero or near zero and fix other sampling settings, and use a seed where the API supports it, though outputs may still vary slightly. Pin the exact model version. Use a structured output format with fixed fields, and reduce ambiguity in the prompt. Cache and store each generated output with its inputs and version, so the stored result, not a regeneration, is what auditors see.
37
What is normalisation, and why are analytics tables often denormalised?
Show answer
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.
38
What is a generator and when would you use one?
Show answer
A function using yield that produces values lazily, one at a time. It saves memory when processing large files or streams because the full sequence is never held in RAM.
39
What is a RAG system and why use it?
Show answer
Retrieval-Augmented Generation fetches relevant documents and gives them to an LLM as context, reducing hallucination and keeping answers grounded in current private data.
40
Write a Python function to flatten a nested list of any depth.
Show answer
def flatten(items):
out = []
for x in items:
if isinstance(x, list):
out.extend(flatten(x))
else:
out.append(x)
return out
print(flatten([1, [2, [3, 4]], 5])) # [1, 2, 3, 4, 5]
Tips to crack the NVIDIA Data Science interview
- Revise C and C++, operating systems and computer architecture, which are core to most NVIDIA engineering roles.
- Learn GPU and CUDA basics, including parallel programming, threads, blocks and the GPU memory hierarchy.
- Read about NVIDIA's data centre and AI platforms to understand where much of its current work is focused.
- 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; NVIDIA interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the NVIDIA Data Science interview?
Candidates usually go through 5 stages: Online assessment or technical screening; Technical round 1; Technical round 2; Technical round 3 or manager 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 NVIDIA Data Science interview?
This practice set leans on Machine Learning (6), Deep Learning and NLP (5), Generative AI and LLMs (5), SQL for Data Science (5). Revise those first, then practise explaining one or two of your own projects end to end, because most NVIDIA interviewers ask you to walk through real work.
Are these the exact questions asked at NVIDIA?
No. These are curated practice questions that match the skills and difficulty usually tested in hardware & semiconductors interviews like NVIDIA's. They are not a leaked or verified list of past questions.
Where does NVIDIA hire Data Science professionals in India?
NVIDIA has large teams in Pune, Bengaluru, Hyderabad. Openings and locations change often, so check the NVIDIA careers page and filter by city and experience.
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