Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About NetApp hiring for Data Science roles
NetApp is a data infrastructure company that provides storage systems, data management software and cloud data services. Its ONTAP storage software and cloud offerings help enterprises store, protect and move data across on-premises and public cloud environments. Its Bengaluru centre is a major engineering site that hires software engineers, quality engineers and support engineers.
For Data Science roles, this NetApp practice set focuses most on Machine Learning (8), SQL for Data Science (7), A/B Testing & Experimentation (5), MLOps and Model Deployment (4). It has 30 medium, 10 hard questions, so you can start easy and work up to the harder ones.
Company type
Product
Headquarters
San Jose, California, USA
Main India offices
Bengaluru
NetApp Data Science interview process
Online coding assessment
Technical round 1 (DSA + C)
Technical round 2 (OS, storage or networking)
Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Write Python code to compare revenue per user between two groups using Welch's t-test.
Common causes are a learning rate that is too high, exploding gradients, bad input values, and unstable maths like log of zero. I would check the input data for NaN or infinite values, lower the learning rate, add gradient clipping, and make sure the loss uses the stable built-in version with logits. With mixed precision, I would check the loss scaler. Logging gradient norms per step helps find the exact point it breaks.
03
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.
Regularization adds a penalty on weights to reduce overfitting. L1 (Lasso) adds |w| and can push features to exactly zero. L2 (Ridge) adds w² and shrinks weights smoothly.
05
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.
06
The business asks you to pick a decision threshold for a churn model. How do you choose it?
The default 0.5 is rarely right. I would get the cost of each outcome: the value of saving a churner, the cost of a retention offer, and the team's capacity to contact customers. Then I compute expected profit or precision and recall at many thresholds on validation data and pick the one that maximises value within capacity. I would also check that the chosen threshold stays stable over recent months.
07
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.
08
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.
09
Every time you rerun yesterday's report, the count of new users changes. What is going on and how do you fix it?
Likely causes are late-arriving events, deduplication running after your query, time zone boundaries or records being updated. First compare runs to see which rows change. Then define the metric on a stable basis: use event time with a fixed cut-off, wait for a data freshness window before publishing, and snapshot the result. Document whether numbers are provisional, and make pipelines idempotent so reruns give the same answer.
10
Write a scikit-learn ColumnTransformer that preprocesses numeric and categorical columns.
Situation: a pricing model had to go live before a sale in ten days. Task: deploy safely in that window. Action: I cut scope to batch scoring instead of real-time, reused our standard Docker and Airflow template, wrote tests for the key features and set up a dashboard for score distribution. Result: it went live two days early and ran without incidents during the sale. Real-time scoring followed a month later.
12
Write SQL to find users who logged in on at least three consecutive days.
WITH d AS (
SELECT DISTINCT user_id, CAST(login_at AS DATE) AS day
FROM logins
),
g AS (
SELECT user_id, day,
day - CAST(ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY day) AS INT) AS grp
FROM d
)
SELECT DISTINCT user_id
FROM g
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;
-- date minus row number is constant within a streak (PostgreSQL syntax)
13
Your dataset is highly imbalanced (2% positives). How do you handle it?
Use stratified splits, class weights, resampling (SMOTE or undersampling) and threshold tuning, and judge the model with PR-AUC or F1 instead of accuracy.
14
Write SQL to find the top 3 products by revenue in each category.
WITH rev AS (
SELECT category, product_id, SUM(price * qty) AS revenue
FROM order_items
GROUP BY category, product_id
), ranked AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY category ORDER BY revenue DESC) AS rn
FROM rev
)
SELECT category, product_id, revenue
FROM ranked
WHERE rn <= 3;
Define the hypothesis and one primary metric, calculate sample size from the baseline, minimum detectable effect, α and power, randomise users, run for full business cycles, check for sample-ratio mismatch, then test both statistical and practical significance.
16
Difference between a shallow copy and a deep copy?
A shallow copy duplicates the outer object but shares nested objects. A deep copy (copy.deepcopy) recursively duplicates everything, so changes to nested items do not leak.
17
Write Python code to fit a Holt-Winters model and forecast the next 12 months.
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.
19
Your sales forecast scores well in k-fold cross-validation but fails in production. What could be wrong with the validation?
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.
20
Soon after a new model version went live, customer complaints spiked. How do you roll back safely and learn from it?
Roll back immediately to the previous registry version through the deployment pipeline, rather than editing servers by hand, and confirm metrics recover. Then investigate offline: compare predictions of both versions on the same traffic, check feature pipelines and data changes, and review what the offline evaluation missed. Add the failing cases to the test set, and use canary or shadow rollout with alert thresholds for future releases.
21
Your forecast uses actual daily temperature as a feature and looks great in backtesting, but it performs worse in production. Why?
In backtesting you used the actual temperature for future days, which you will never know at forecast time. In production the model gets a weather forecast, which has its own error, so performance drops. This is a subtle form of leakage. Fix the backtest by using the weather forecast that was available on each forecast date, or lagged actuals, so offline results match what production can really achieve.
22
How do you pull large data from a SQL database into Pandas efficiently?
Push as much work as possible into SQL: filter, aggregate and select only needed columns before pulling. Use pd.read_sql with a SQLAlchemy engine and chunksize to stream large results. For very large extracts, export to Parquet from the warehouse and read that, which is much faster than row-by-row transfer. Set dtypes like category early, and never use SELECT star on a big table from a notebook.
23
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
import torch, torch.nn as nn
from torchvision import models
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
for p in model.parameters():
p.requires_grad = False # freeze backbone
model.fc = nn.Linear(model.fc.in_features, 5)
opt = torch.optim.AdamW(model.fc.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()
# train head first, then unfreeze later layers with a lower lr
24
How does Kubernetes help in serving machine learning models?
Kubernetes runs containerised model services across a cluster. It restarts failed containers, scales the number of replicas based on traffic or CPU, rolls out new versions gradually and can roll back, and schedules GPU workloads on the right nodes. Tools built on it, like KServe or Seldon, add model-specific features such as canary releases and autoscaling to zero. The trade-off is operational complexity, so small teams often use managed services.
25
What is the difference between apply, map and applymap in Pandas?
map works element-wise on a Series, apply works on a Series or along a DataFrame axis, and applymap (now DataFrame.map) applies a function to every cell.
26
Tell me about a time you automated a manual step in a machine learning workflow.
Situation: each monthly retrain needed a day of manual data pulls, notebook runs and copying files to servers. Task: make it reliable and hands-off. Action: I wrapped the steps into a scheduled pipeline with data validation checks, logged runs in MLflow and added an approval step before promotion. Result: retraining time dropped from a day to under an hour of review, and there were no more failed deployments from missed steps.
27
What is interference or a network effect in experiments, and why does it break standard A/B tests?
Standard A/B analysis assumes one user's outcome does not depend on another user's assignment, the SUTVA assumption. In social or messaging products, a treated user's new feature affects their untreated friends, so control is contaminated and the effect is biased. Solutions include cluster randomisation, where connected groups of users are assigned together, ego-network designs and comparing results across different cluster sizes to estimate the spillover.
Gradients shrink as they pass through many layers (for example with sigmoid), so early layers barely learn. ReLU, residual connections and good initialisation help.
29
Your RAG chatbot often answers using the wrong documents. How do you fix it?
First check retrieval on its own with a labelled set of questions, since generation cannot fix bad context. Then improve chunking so passages are self-contained, add metadata filters such as product or date, try a better embedding model, and add hybrid search with keywords. A reranker on the top 20 results often helps most. Also rewrite vague user questions into clearer search queries before retrieval.
30
What are precision and recall trade-offs in practice?
Raising the threshold improves precision but lowers recall. Fraud detection leans to recall, while spam filters often prefer precision; choose by the cost of each error.
31
Write a Python function to split a long text into overlapping chunks by word count for a RAG index.
This is overfitting. Check for data leakage, simplify the model, add regularization, get more data or augment it, use cross-validation, early stopping and feature selection.
Use grid search, random search or Bayesian optimisation with cross-validation, starting with a coarse random search and narrowing the promising ranges.
34
Tell me about a time an experiment result changed a product decision.
Situation: the team was confident a redesigned home page would increase orders. Task: I owned the experiment design and analysis. Action: I set conversion as primary and session length and refunds as guardrails, ran it for two full weeks and found no lift in orders but a drop for older app versions. Result: we fixed the performance issue, retested, and shipped a version that raised orders by about 1.5 percent.
35
Tell me about a time your forecast was badly wrong. How did you handle it?
Situation: my weekly demand forecast under-predicted a category by 30 percent for a month. Task: explain the miss and stop it recurring. Action: I compared forecast and actuals by region and found a new distribution channel had launched that the model knew nothing about. I told the planning team quickly, added channel as a feature and set up a weekly error review. Result: error returned to normal and planners trusted the alerts.
Use stratified splits, class weights, resampling such as SMOTE, threshold tuning and judge models with precision, recall, F1 or PR-AUC instead of accuracy.
37
What is a long-term holdout group and when is it useful?
A holdout is a small share of users, say 2 to 5 percent, kept away from a set of launched features for months. Comparing them with everyone else measures the cumulative, long-term impact of many shipped changes, which individual short tests can overstate because of novelty effects or winner's curse. It is useful for recommendation systems, notifications and marketing, but it costs some value from users who miss improvements.
Accuracy, F1 and confusion matrices for classification, BLEU or ROUGE for generation and summarisation, perplexity for language models, plus human review.
39
Write SQL to compute a running total of spend for each user.
SELECT user_id, order_date, amount,
SUM(amount) OVER (
PARTITION BY user_id
ORDER BY order_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS running_spend
FROM orders
ORDER BY user_id, order_date;
40
Write SQL to compute week-1 retention by signup week.
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;
Tips to crack the NetApp Data Science interview
Revise C programming, memory management, operating systems and file systems, which are frequently tested for storage engineering roles.
Learn storage basics such as RAID, snapshots, replication and protocols like NFS, SMB and iSCSI before interviews.
Practise DSA problems and be ready to debug code or explain behaviour of a program step by step.
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; NetApp interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the NetApp Data Science interview?
Candidates usually go through 4 stages: Online coding assessment; Technical round 1 (DSA + C); Technical round 2 (OS, storage or networking); 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 NetApp Data Science interview?
This practice set leans on Machine Learning (8), SQL for Data Science (7), A/B Testing & Experimentation (5), MLOps and Model Deployment (4). Revise those first, then practise explaining one or two of your own projects end to end, because most NetApp interviewers ask you to walk through real work.
Are these the exact questions asked at NetApp?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like NetApp's. They are not a leaked or verified list of past questions.
Where does NetApp hire Data Science professionals in India?
NetApp has large teams in Bengaluru. Openings and locations change often, so check the NetApp careers page and filter by city and experience.