01
Which activation functions do you know and when to use them?
Show answer
ReLU for hidden layers, sigmoid for binary outputs, softmax for multi-class outputs and tanh when zero-centred outputs help; Leaky ReLU avoids dead neurons.
Sprinklr Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Sprinklr hiring for Data Science roles
Sprinklr is an enterprise software company that offers a unified customer experience management platform covering social media, customer service, marketing and research. Large brands use it to manage customer conversations across many digital channels. It was founded by Ragy Thomas and has a large engineering presence in India, where it hires software engineers, product analysts, support engineers and implementation consultants.
For Data Science roles, this Sprinklr practice set focuses most on Machine Learning (8), A/B Testing & Experimentation (6), Generative AI and LLMs (5), Computer Vision (4). It has 1 easy, 29 medium, 10 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Product
- Headquarters
- New York, USA
- Main India offices
- Gurugram, Bengaluru
Sprinklr Data Science interview process
- Online coding assessment
- Technical round 1 (DSA)
- Technical round 2 (design or CS fundamentals)
- Hiring manager round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
What is DBSCAN and when is it better than K-means?
Show answer
DBSCAN is a density-based clustering algorithm. It groups points that have at least min_samples neighbours within distance eps, and marks isolated points as noise. Unlike K-means, it does not need the number of clusters, finds clusters of any shape, and handles outliers. It struggles when clusters have very different densities and in high dimensions. It suits location data, like finding delivery hotspots from GPS points.
03
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.
04
Annotators labelling your images often disagree with each other. How do you handle this?
Show answer
First measure agreement, for example with Cohen's kappa or IoU between annotators' boxes, to find which classes are confusing. Usually the guidelines are unclear, so write precise rules with example images for edge cases and retrain annotators. Have multiple people label a subset and resolve conflicts by majority vote or an expert reviewer. Track ambiguous images separately, since even a perfect model cannot beat human label noise.
05
Explain the KNN algorithm and its drawbacks.
Show answer
It predicts from the k nearest labelled points. It is simple but slow at prediction time, sensitive to scaling and irrelevant features, and weak in high dimensions.
06
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.
07
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.
08
What is a feature store and what problems does it solve?
Show answer
A feature store is a central system to define, compute, store and serve features for models. It keeps an offline store for training with point-in-time correct history and an online store for low-latency serving, both fed by the same feature definitions. This prevents training-serving skew, avoids teams rebuilding the same features, and adds documentation and monitoring. It is worth it when many models share features or need real-time serving.
09
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.
10
What are interaction features and when do they help?
Show answer
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.
11
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
12
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.
13
You need to extract fields from scanned invoices that come in hundreds of different layouts. How would you design it?
Show answer
Fixed templates will not scale to hundreds of layouts. Start with preprocessing: deskew, denoise and detect text regions. Run OCR to get words with their positions. Then use a layout-aware document model, or a vision-language model, to map words to fields like invoice number, GSTIN and total. Add validation rules, such as GSTIN format and line items summing to the total, and send low-confidence documents to human review.
14
Your neural network's training loss suddenly becomes NaN. What do you check?
Show answer
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.
15
How do you handle imbalanced classes?
Show answer
Use stratified splits, class weights, resampling such as SMOTE, threshold tuning and judge models with precision, recall, F1 or PR-AUC instead of accuracy.
16
How do you evaluate a RAG system?
Show answer
Evaluate retrieval and generation separately. For retrieval, build a set of real questions with known relevant documents and measure recall at k and mean reciprocal rank. For generation, measure faithfulness to retrieved context, answer correctness and relevance, using human review on a sample and an LLM-as-judge with a clear rubric at scale. Track latency and cost too. Rerun the same evaluation set after every change to prompts, chunking or models.
17
A 10 million row DataFrame with a city column is using too much memory. How can the category dtype help?
Show answer
The category dtype stores each unique value once and keeps small integer codes for every row. For a column like city with 50 unique values in 10 million rows, this can cut memory by more than 90 percent and speed up groupby and sorting. It helps when a column has low cardinality. It does not help for unique IDs or free text, and adding new categories later needs extra handling.
18
Tell me about the most challenging machine learning project you have worked on.
Show answer
Situation: I built a lead scoring model where only 3 percent of leads converted and labels came 60 days late. Task: deliver a useful ranking for the sales team. Action: I set up a clean time-based validation, engineered activity features, used gradient boosting with class weights and measured lift in the top decile. Result: the top 10 percent of scored leads converted 3.5 times better than average. The hardest part was building trust in the labels.
19
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)))
20
What are heterogeneous treatment effects, and what are the risks of slicing results by segment?
Show answer
Heterogeneous treatment effects mean the change helps some segments more than others, for example new users but not repeat users. Finding them can guide targeting. The risk is that slicing by many segments creates false discoveries, and each segment has less power, so differences may be noise. Pre-register key segments, correct for multiple comparisons, and validate any surprising segment effect in a follow-up test before acting on it.
21
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.
22
What is multicollinearity and how do you handle it?
Show answer
Predictors that are highly correlated make coefficients unstable. Detect with VIF above about 5-10, then drop or combine features, use PCA or apply regularisation.
23
What is chunking in RAG and how do you choose a chunk size?
Show answer
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.
24
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
Show answer
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
25
Your model performance degrades every few months. Would you retrain on a fixed schedule or based on triggers?
Show answer
Scheduled retraining, like monthly, is simple and predictable but may be too late after a sudden change or wasteful when nothing changed. Trigger-based retraining runs when monitoring shows drift or a performance drop, which reacts faster but needs reliable monitoring. Many teams combine both: a regular schedule plus triggers for big shifts. Either way, a retrained model must pass the same evaluation gate before replacing the current one.
26
Why do ratio metrics like click-through rate per user need special variance calculation?
Show answer
A metric like total clicks divided by total page views has a numerator and denominator that both vary by user, and page views within a user are correlated. Treating each page view as independent underestimates variance and inflates false positives. The delta method approximates the variance of a ratio of means using the variances and covariance of the per-user numerator and denominator. Bootstrap by user is an alternative when formulas get complicated.
27
Tell me about a feature you engineered that significantly improved a model.
Show answer
Situation: our delivery time model kept underestimating late-night orders. Task: reduce large errors. Action: I noticed restaurant prep time depended on how busy the kitchen was, so I built a feature for active orders at that restaurant in the last 30 minutes, computed point in time. Result: MAE dropped by 14 percent and late-night errors fell the most. It taught me to study the worst errors before adding generic features.
28
How do you handle personal data when sending customer text to an external LLM API?
Show answer
First check the data processing agreement, data residency and retention terms, and India's DPDP Act requirements with legal. Minimise what is sent: mask or tokenise names, phone numbers, Aadhaar and card numbers before the call, and restore them afterwards if needed. Use enterprise endpoints with no training on your data, log access, and consider a self-hosted open model for highly sensitive workloads.
29
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.
30
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?
Show answer
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.
31
For a warehouse, a stockout costs five times more than holding extra stock. How should the forecast change?
Show answer
A forecast of the mean is the wrong target when costs are asymmetric. Forecast a quantile instead. The optimal quantile here is about 5 divided by 5 plus 1, roughly the 83rd percentile, which is the newsvendor critical ratio. Train with quantile or pinball loss, or use prediction intervals from a probabilistic model. Evaluate with pinball loss and the actual stockout and holding cost, not just MAE.
32
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}')
33
What is a long-term holdout group and when is it useful?
Show answer
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.
34
How do you work with domain experts to find good features?
Show answer
Situation: on an insurance claims model, my first features gave average results. Task: improve fraud detection. Action: I sat with two claim investigators for a day and asked what made them suspicious. They mentioned claims filed soon after policy start and repeated garage names. I turned these into features and checked them with the team. Result: recall at the same precision rose by 12 points. Experts often know signals the data hides.
35
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.
36
Your random forest is accurate but too slow at prediction time for a real-time API. What do you do?
Show answer
First measure where the time goes. Then reduce trees and maximum depth and check the accuracy loss. Switch to a gradient boosting model, which often needs fewer, smaller trees. Compile the model with tools like ONNX Runtime or Treelite. Cache features or precompute scores for known users. If accuracy allows, a simpler logistic regression may meet the latency budget easily. Always compare options on the same validation set.
37
Your deployed model gives different predictions than the same model in your notebook. Why?
Show answer
Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.
38
Write SQL to compute a running total of spend for each user.
Show answer
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;
39
Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.
Show answer
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import brier_score_loss
base = HistGradientBoostingClassifier()
cal = CalibratedClassifierCV(base, method='isotonic', cv=5)
cal.fit(X_train, y_train)
prob = cal.predict_proba(X_test)[:, 1]
print('Brier:', brier_score_loss(y_test, prob))
40
Halfway through a test you discover a bug that broke the treatment for Android users. What do you do?
Show answer
Stop or pause the affected part, fix the bug and restart the test with fresh data, since mixing broken and fixed periods biases results. Do not simply drop Android users from analysis afterwards, because that can break randomisation balance if the bug affected behaviour or logging. Document the incident, and add pre-launch checks such as running the variant through QA on every platform and monitoring guardrails in the first hours.
Tips to crack the Sprinklr Data Science interview
- Practise medium-level DSA problems on arrays, trees, graphs and dynamic programming, as coding rounds can be fairly demanding.
- Revise operating systems, DBMS and basic system design, and explain trade-offs clearly when discussing your project architecture.
- Learn about customer experience management and social media tools to explain how Sprinklr's platform helps large enterprises.
- 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; Sprinklr interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Sprinklr Data Science interview?
Candidates usually go through 5 stages: Online coding assessment; Technical round 1 (DSA); Technical round 2 (design or CS fundamentals); Hiring 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 Sprinklr Data Science interview?
This practice set leans on Machine Learning (8), A/B Testing & Experimentation (6), Generative AI and LLMs (5), Computer Vision (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Sprinklr interviewers ask you to walk through real work.
Are these the exact questions asked at Sprinklr?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Sprinklr's. They are not a leaked or verified list of past questions.
Where does Sprinklr hire Data Science professionals in India?
Sprinklr has large teams in Gurugram, Bengaluru. Openings and locations change often, so check the Sprinklr careers page and filter by city and experience.
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