01
Explain Bayes' theorem with an example.
Show answer
P(A given B) = P(B given A) x P(A) / P(B). In a medical test it converts test accuracy and disease prevalence into the real chance a positive result means disease.
Data Science Interview Questions in Nagpur
36 practice questions with answers for Data Science interviews in Nagpur.
Data Science jobs in Nagpur
Nagpur's MIHAN SEZ hosts IT-services and BFSI delivery centres, giving central India a steady market for data and QA roles. Interviews are usually service-company style, with aptitude, technical and HR rounds.
Below are 36 questions that match the mix of service, product and domain employers hiring data scientists in Nagpur.
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02
What are RNNs and why did LSTMs improve them?
Show answer
RNNs process sequences with a hidden state but struggle with long dependencies. LSTMs add gates that control what to remember or forget, easing the vanishing gradient.
03
Why do we use activation functions? Why is ReLU popular?
Show answer
Activations add non-linearity so networks can learn complex patterns. ReLU (max(0, x)) is cheap and reduces vanishing gradients compared with sigmoid/tanh; Leaky ReLU avoids 'dead' neurons.
04
What is non-maximum suppression in object detection?
Show answer
A detector usually predicts many overlapping boxes for the same object. Non-maximum suppression keeps the box with the highest confidence, removes other boxes of the same class whose IoU with it exceeds a threshold such as 0.5, and repeats with the remaining boxes. A threshold that is too low can delete genuinely nearby objects in crowded scenes, so variants like soft-NMS lower scores instead of removing boxes.
05
How does linear regression work and what are its assumptions?
Show answer
It fits a line minimising squared error. Assumes linearity, independent errors, constant variance (homoscedasticity), normally distributed residuals and no strong multicollinearity.
06
Tell me about a GenAI project you built and what you learned from it.
Show answer
Situation: our HR team answered the same leave and policy questions daily. Task: build an internal assistant in six weeks. Action: I built a RAG pipeline over policy PDFs, added citations, created a 150-question evaluation set with HR, and iterated on chunking and reranking. Result: answer accuracy reached 91 percent and HR queries dropped by 40 percent. I learned that the evaluation set mattered more than the choice of model.
07
Tell me about a time you deployed a model under a tight deadline.
Show answer
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.
08
What is statistical power and how can you increase it?
Show answer
Power is the probability of detecting a real effect. Increase it with a larger sample, a bigger effect size, lower noise or a higher significance level.
09
Explain exponential smoothing and the Holt-Winters method.
Show answer
Simple exponential smoothing forecasts with a weighted average of past values where weights decay exponentially, so recent points matter more. Holt's method adds a smoothed trend component. Holt-Winters adds a seasonal component as well, either additive or multiplicative. Each component has a smoothing parameter between 0 and 1 that is fitted to the data. These models are fast, easy to explain and strong baselines for retail and demand data.
10
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
11
Difference between a shallow copy and a deep copy?
Show answer
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.
12
Write SQL to show each customer's first order date, last order date and the gap between them in days.
Show answer
SELECT
customer_id,
MIN(order_date) AS first_order,
MAX(order_date) AS last_order,
MAX(order_date) - MIN(order_date) AS days_between,
COUNT(*) AS orders
FROM orders
GROUP BY customer_id
ORDER BY days_between DESC;
-- PostgreSQL: DATE minus DATE gives days; use DATEDIFF in other engines
13
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.
14
What are precision and recall trade-offs in practice?
Show answer
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.
15
Tell me about a time you automated a manual step in a machine learning workflow.
Show answer
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.
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
A feature won its A/B test with a 4 percent lift, but after full launch the metric barely moved. Why might that happen?
Show answer
Several reasons are common. Winner's curse: significant results from underpowered tests tend to overestimate the true effect. Novelty effect faded after launch. Seasonality or other launches changed the baseline, so a before-after comparison is misleading. The test population may differ from all users, for example only one platform. Use a holdout after launch to measure real impact, and run tests with adequate power and duration.
18
What is a contrastive image-text model like CLIP, and how does it enable zero-shot classification?
Show answer
CLIP trains an image encoder and a text encoder together on hundreds of millions of image-caption pairs, pulling matching pairs close in a shared embedding space and pushing mismatches apart. For zero-shot classification you embed prompts like 'a photo of a damaged parcel' for each class and pick the class whose text embedding is most similar to the image embedding. It works without task-specific training but may need fine-tuning for niche domains.
19
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.
20
Users say your internal chatbot gives outdated policy answers. What is the likely cause and fix?
Show answer
The knowledge index probably still holds old document versions, or the model is answering from its training data instead of retrieved context. Fix the ingestion pipeline to re-index on document change and remove superseded versions. Store effective dates in metadata and filter or boost the latest. Instruct the model to answer only from provided context and show the source with its date, so users can verify it.
21
What is regularization? Explain L1 vs L2.
Show answer
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.
22
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.
23
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
24
What is binning and when is it useful?
Show answer
Binning groups a continuous variable into ranges, like age into 18-25, 26-35 and so on. It can capture non-linear effects in linear models, reduce the impact of outliers and make results easier to explain to business teams. Bins can be equal width, equal frequency using quantiles, or chosen by domain knowledge. The downside is losing information inside each bin, and tree models usually do not need it.
25
Why are NumPy arrays faster than Python lists?
Show answer
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
26
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.
27
Write a FastAPI endpoint that serves predictions from a saved scikit-learn model.
Show answer
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import pandas as pd
app = FastAPI()
model = joblib.load('model.joblib')
class Customer(BaseModel):
tenure: int
monthly_spend: float
plan: str
@app.post('/predict')
def predict(c: Customer):
X = pd.DataFrame([c.model_dump()])
return {'churn_probability': float(model.predict_proba(X)[0, 1])}
28
What are quantile and power transforms such as Box-Cox and Yeo-Johnson, and when do you use them?
Show answer
Power transforms reshape a skewed feature to look more normal. Box-Cox needs strictly positive values, while Yeo-Johnson also handles zero and negative values; both learn a lambda parameter from training data. A quantile transform maps values to a uniform or normal distribution by rank, which is robust to outliers. They help linear models, distance-based methods and neural networks, but tree models rarely need them.
29
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.
30
Write SQL to find the second highest salary in an employees table.
Show answer
SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
-- alternative with window function
SELECT DISTINCT salary
FROM (SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk
FROM employees) t
WHERE rnk = 2;
31
How do you detect and treat concept drift?
Show answer
Monitor input distributions and live metrics, compare to training baselines, and retrain or recalibrate on fresh data when performance drops.
32
What is SVM and the kernel trick?
Show answer
SVM finds the maximum-margin separating hyperplane. Kernels such as RBF compute similarity in a higher-dimensional space implicitly, so non-linear boundaries are possible.
33
Soon after a new model version went live, customer complaints spiked. How do you roll back safely and learn from it?
Show answer
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.
34
Tell me about a time you found a mistake in your own analysis after sharing it.
Show answer
Situation: after presenting a retention report, I noticed I had counted test accounts as users. Task: correct it before decisions were made. Action: I reran the numbers that evening, informed my manager and the audience with a short note showing old and new figures and what changed, and added a filter and a check to the shared query. Result: the conclusion held with smaller numbers, and the team valued the transparency.
35
CNN vs RNN vs Transformer — what is the difference?
Show answer
CNNs use convolutions to find spatial patterns (images). RNNs/LSTMs process sequences step by step but struggle with long context. Transformers use self-attention to look at all tokens at once — parallel and better at long-range context; they power modern LLMs.
36
What is the attention mechanism?
Show answer
It lets a model weigh which input positions matter most for each output, computing weighted combinations of values from query-key similarity. It is the core of transformers.
Companies with offices in Nagpur (2)
Few big employers listed in Nagpur?
Many Nagpur candidates also apply to teams in Pune and to remote or hybrid roles. Practise the Pune set as well.
Frequently asked questions
Which companies hire Data Science professionals in Nagpur?
Companies with large offices in Nagpur include Persistent Systems, GlobalLogic. Use their company pages below to practise employer-specific sets.
Are Data Science interviews in Nagpur different from other cities?
The questions depend more on the company than the city. Nagpur's mix of employers decides the style: Interviews are usually service-company style, with aptitude, technical and HR rounds
How do I find Data Science jobs in Nagpur?
Shortlist companies with Nagpur offices, keep your CV focused on Data Science projects, apply on company career pages and job portals with the Nagpur filter, and practise the questions on this page before each interview.
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