36 practice questions with answers for Data Science interviews in Chandigarh.
Data Science jobs in Chandigarh
Chandigarh, Mohali and Panchkula form the Tricity IT cluster, with IT-services firms, SaaS companies and start-ups in Mohali's IT City and Chandigarh's IT Park. Interviews usually check strong fundamentals and practical tool skills.
Below are 36 questions that match the mix of service, product and domain employers hiring data scientists in Chandigarh.
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01
What is the difference between iterators and iterables in Python?
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.
02
What is a model registry and why do teams use one?
A model registry is a central store for trained model versions along with their metadata: training data version, code commit, parameters, metrics and owner. Models move through stages such as staging, production and archived, with approvals. It gives one source of truth for which model is live, makes rollback to a previous version easy and supports audits. Tools like MLflow Model Registry are commonly used.
03
What is the difference between semantic, instance and panoptic segmentation?
Semantic segmentation gives each pixel a class, such as road or car, but does not separate two cars. Instance segmentation separates individual objects of countable classes, giving each car its own mask. Panoptic segmentation combines both: every pixel gets a class, and countable objects also get an instance ID, while background stuff like sky stays semantic. Autonomous driving and robotics often need panoptic or instance output.
04
Your power analysis says you need 200,000 users per group, but the page gets only 5,000 users a week. What are your options?
Waiting 80 weeks is unrealistic. Options: test a bolder change so the expected effect is larger; use a more sensitive metric closer to the change, like add-to-cart instead of purchase; apply CUPED to reduce variance; or move the test to a higher-traffic page. If none work, accept a larger MDE explicitly, or decide using qualitative research and a staged rollout with monitoring instead of a formal test.
05
Which activation functions do you know and when to use them?
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.
06
What do ACF and PACF plots tell you about a time series?
The ACF plot shows correlation between the series and its own lags, including indirect effects through intermediate lags. The PACF shows the correlation at each lag after removing the effect of shorter lags. For a stationary series, a PACF that cuts off after lag p suggests an AR(p) term, and an ACF that cuts off after lag q suggests an MA(q) term. Spikes at lag 7 or 12 reveal weekly or yearly seasonality.
07
Write scikit-learn code to get precision, recall and a confusion matrix at a custom threshold of 0.3.
Situation: the leadership team needed festive-sale numbers by city for a 9 am review, and the dashboard pipeline had failed overnight. Task: deliver accurate numbers in three hours. Action: I queried the raw orders tables directly, reused validated logic from the dashboard SQL, cross-checked totals with finance's daily figure and flagged two cities with partial data. Result: the review went ahead on time, and I later added pipeline failure alerts.
09
How do you turn text columns into features for a tabular model?
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.
The probability of seeing a result at least as extreme as the observed one if the null hypothesis were true. A small p-value is evidence against the null, not proof that the alternative is true.
11
What is an Overall Evaluation Criterion (OEC) in experimentation?
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.
12
What is statistical power and how can you increase it?
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.
13
What is Bayesian A/B testing and how are its results reported?
Bayesian A/B testing starts with a prior on each variant's metric, updates it with observed data and produces a posterior distribution. Instead of a p-value, you report things like the probability that treatment beats control, say 94 percent, and the expected loss if you choose the wrong variant. Business teams find this easier to interpret. Priors must be chosen sensibly, and continuous monitoring still needs discipline about decision rules.
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.
15
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.
16
What is correlation and why does it not imply causation?
Correlation measures linear association between variables (-1 to 1). A third factor, reverse causality or chance can create it, so causation needs controlled experiments.
17
What are naive and seasonal naive forecasts, and why should you always compute them?
A naive forecast predicts that the next value equals the last observed value. A seasonal naive forecast predicts the value from the same period last season, such as last Monday for next Monday. They take seconds to build and are surprisingly hard to beat on many business series. Always report your model's error relative to these baselines; if a complex model cannot beat seasonal naive, it is not adding value.
18
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.
19
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
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.
21
Explain exponential smoothing and the Holt-Winters method.
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.
22
A key metric dropped 8 percent week over week. How do you check whether it is real?
First rule out data issues: tracking changes, pipeline delays, missing partitions or a changed metric definition. Then compare with the same week last year and look for holidays or seasonality. Check normal weekly variation to see if 8 percent is unusual. Break the drop down by platform, region, app version and user type to find where it is concentrated. Finally, check releases, campaigns or outages that happened that week.
23
Your logging system was down for four days, leaving gaps in a daily series. How do you handle them before modelling?
First make the gaps explicit by reindexing to a full daily calendar so missing days appear as NaN instead of silently disappearing. For short gaps, linear or time-based interpolation is usually fine. If there is strong weekly seasonality, fill using the same weekday from neighbouring weeks or a seasonal decomposition. Add a flag column marking imputed days, and never treat missing as zero unless zero really happened.
24
Tell me about a GenAI project you built and what you learned from it.
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.
Gradient boosting builds trees one after another. It starts with a simple prediction, like the mean. Each new small tree is fitted to the negative gradient of the loss, which for squared error is just the residuals. Its predictions are multiplied by a learning rate and added to the model. Repeating this slowly reduces error. Key settings are number of trees, learning rate, tree depth and subsampling, which together control overfitting.
26
You keep adding features and R-squared keeps rising. Is the model really getting better?
R-squared shows the share of variance in the target explained by the model. It never decreases when you add a feature, even a useless one. Adjusted R-squared penalises extra features based on the number of predictors and sample size, so it rises only if a new feature improves the model more than chance would. Use adjusted R-squared to compare linear models with different numbers of features.
27
How do you deploy and monitor a machine learning model?
Package the model behind an API with versioning, track latency and errors, monitor input drift and prediction quality, and automate retraining with rollback.
An INNER JOIN returns only rows with matching keys in both tables. A LEFT JOIN returns all rows from the left table, with NULLs where the right has no match. A RIGHT JOIN is the mirror of that. A FULL OUTER JOIN returns all rows from both sides, matched where possible. In analytics, LEFT JOIN from a customers table is common so customers with no orders are still counted.
29
What are quantile and power transforms such as Box-Cox and Yeo-Johnson, and when do you use them?
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.
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.
31
Tell me about a computer vision project where data quality was the biggest challenge.
Situation: a shelf-detection model plateaued at low accuracy. Task: find out why and fix it. Action: I reviewed 500 errors and found that a third were labelling mistakes, with inconsistent boxes for partly hidden products. I rewrote the guidelines with visual examples, had a second annotator review hard images and relabelled the worst batch. Result: mAP rose by 9 points without changing the model, and labelling rework dropped sharply.
Discriminative models learn P(y given x) directly (logistic regression, SVM). Generative models learn the joint P(x, y) and can generate data (Naive Bayes, GANs).
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.
Calibration checks whether predicted probabilities match real frequencies (e.g. 70% predictions are right about 70% of the time). Fix with Platt scaling or isotonic regression.
Which companies hire Data Science professionals in Chandigarh?
Companies with large offices in Chandigarh include Grant Thornton Bharat. Use their company pages below to practise employer-specific sets.
Are Data Science interviews in Chandigarh different from other cities?
The questions depend more on the company than the city. Chandigarh's mix of employers decides the style: Interviews usually check strong fundamentals and practical tool skills
How do I find Data Science jobs in Chandigarh?
Shortlist companies with Chandigarh offices, keep your CV focused on Data Science projects, apply on company career pages and job portals with the Chandigarh filter, and practise the questions on this page before each interview.