Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Zomato hiring for Machine Learning roles
Zomato is an Indian food delivery and restaurant discovery platform, operating under the listed parent Eternal Ltd alongside Blinkit quick commerce and District. Its tech and analytics teams work on delivery logistics, restaurant listings, ads and customer experience. It hires for software engineers, SDETs, product and business analysts, and data scientists.
For Machine Learning roles, this Zomato practice set focuses most on Regression Models (7), Model Evaluation and Tuning (6), ML Fundamentals (5), Decision Trees and Ensembles (4). It has 3 easy, 20 medium, 7 hard questions, so you can start easy and work up to the harder ones.
Company type
E-commerce & consumer tech
Headquarters
Gurugram, India
Main India offices
Gurugram
Zomato Machine Learning interview process
Online assessment or assignment
Technical or case round
Hiring manager round
Culture fit round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for ZomatoStart practicing
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A model registry is a central store of model versions with metadata: training data version, parameters, metrics, owner and stage such as staging or production. It makes promotions and rollbacks controlled and auditable, and lets serving systems load a model by name and version. MLflow Model Registry, SageMaker Model Registry and Vertex AI Model Registry are common examples.
from sklearn.model_selection import LearningCurveDisplay
import numpy as np
LearningCurveDisplay.from_estimator(model, X, y, cv=5,
train_sizes=np.linspace(0.1, 1.0, 6), scoring='roc_auc', n_jobs=-1)
plt.show()
A persistent gap between curves suggests more data or regularisation; two low curves suggest a richer model or better features.
03
How do you implement simple gradient descent for linear regression in NumPy?
Compute predictions, the gradient of MSE, and update weights in a loop.
import numpy as np
w = np.zeros(X.shape[1]); b = 0.0; lr = 0.01
for _ in range(1000):
err = X @ w + b - y
w -= lr * (2 / len(y)) * (X.T @ err)
b -= lr * (2 / len(y)) * err.sum()
Scale features first or the loop converges slowly.
04
Lasso dropped a feature the business considers very important. How do you handle it?
Lasso often keeps only one of a group of correlated features, so the dropped feature may be redundant rather than useless. I would check correlations, try Elastic Net which keeps correlated groups, and compare cross-validated performance with the feature forced in. I would explain the trade-off clearly; if the accuracy difference is small, keeping the feature for trust and interpretability is reasonable.
05
You need prediction intervals, not just point estimates, for delivery time. How?
Options are quantile regression, training separate models for the 10th and 90th percentiles with GradientBoostingRegressor(loss='quantile') or LightGBM; conformal prediction, which wraps any model and gives intervals with guaranteed coverage; or bootstrapping. I would check calibration: about 80 percent of actual values should fall within an 80 percent interval on a holdout set.
06
Check calibration with a reliability curve and Brier score.
from sklearn.calibration import CalibrationDisplay
from sklearn.metrics import brier_score_loss
proba = clf.predict_proba(X_test)[:, 1]
print('Brier', brier_score_loss(y_test, proba))
CalibrationDisplay.from_predictions(y_test, proba, n_bins=10)
plt.show()
Points on the diagonal mean predicted probabilities match observed frequencies.
Fix random seeds in NumPy, Python and the model, pin library versions in requirements.txt, version the data or record its hash, and save the exact code commit and hyperparameters. Tools like MLflow log parameters, metrics and artifacts per run. Use pipelines so preprocessing is identical each time, and keep the train-test split stored rather than recreated randomly.
08
Your house price model's residuals grow larger for expensive houses. What do you do?
That is heteroscedasticity, typical for price data that is right-skewed. I would model log(price) instead of price, which turns absolute errors into roughly percentage errors and stabilises variance. Then I would convert predictions back with exp and check residual plots again. Evaluating with RMSLE or MAPE also fits better when relative error matters more than absolute error.
An SVM finds the hyperplane that separates classes with the maximum margin, and the points on the margin are the support vectors. The soft-margin version uses C to trade off margin width against misclassification. Kernels like RBF or polynomial let SVMs learn non-linear boundaries by implicitly mapping data to higher dimensions. SVMs need scaled features and become slow beyond roughly a hundred thousand rows.
10
Train a random forest and show feature importances in scikit-learn.
It depends on error cost. The first model makes fewer big errors, while the second is better on typical cases but has some large misses. If large errors are expensive, such as stock-outs of costly items, choose the first. If typical accuracy matters more, choose the second. I would also check error distributions by segment and discuss with stakeholders before deciding.
12
XGBoost validation loss starts rising after 300 rounds while training loss keeps falling. What do you do?
That is overfitting beyond about 300 rounds. I would use early stopping with a patience such as 50 rounds on a validation set so training stops at the best iteration, then lower the learning rate, reduce max_depth, add subsample and colsample_bytree below 1, and increase min_child_weight or lambda. The final model is refit using the best iteration count found.
13
Tell me about a time a production model failed and how you communicated it.
Situation: our daily scoring job silently produced all-zero scores for two days after an upstream column was renamed. Task: fix it and inform affected teams. Action: I rolled back to cached scores, patched the feature mapping, and sent a short note explaining impact, cause and fix. I then added schema validation and a check on score distribution. Result: the issue never repeated, and teams trusted our alerts more.
14
How do you create lag and rolling features for a sales forecasting model in pandas?
Sort by item and date, then shift so features use only past values.
df = df.sort_values(['item', 'date'])
g = df.groupby('item')['sales']
df['lag_7'] = g.shift(7)
df['roll_mean_28'] = g.transform(lambda s: s.shift(1).rolling(28).mean())
The shift before rolling prevents the current day's sales from leaking into its own feature.
15
k-means gives one huge cluster and several tiny ones. What could be wrong?
Likely causes are unscaled features, where one large-range feature dominates distance; heavy skew or outliers pulling centroids; or data that is not naturally spherical. I would log-transform and scale features, remove or cap extreme outliers, and try different k values. If shapes are irregular, DBSCAN, HDBSCAN or a Gaussian Mixture Model may fit better.
16
How do you handle disagreement with a senior colleague about model choice?
I keep it about evidence. In one project a senior preferred a complex model while I proposed regularised regression. I suggested we compare both on the same cross-validation folds and also measure inference time and explainability. The complex model was only 1 percent better, so we chose the simpler one. I made sure to credit his idea of the extra features, which we kept.
17
Your training loss is not decreasing at all. What could be wrong?
Common causes are a learning rate that is too high or too low, unscaled features, a bug in labels or feature alignment, wrong loss for the task, or data that has been shuffled away from its labels. I would try overfitting a tiny subset first; if the model cannot even fit 50 rows, there is a bug. Then check scaling, learning rate and gradient values.
18
40 percent of values in an important income column are missing. What do you do?
I would study the missingness pattern first, for example whether self-employed applicants skip it. I would add a binary was_missing feature, impute with the median within relevant groups or use IterativeImputer, and compare against models that handle missing values natively, like HistGradientBoosting or XGBoost. I would also raise the issue with the data owner so collection can improve.
Compare metrics across groups such as gender, age band or region: approval rate (demographic parity), true positive rate (equal opportunity) and false positive rate. Check calibration per group too. Remove or audit proxy features like pin code that may stand in for protected attributes. Document findings in a model card, and involve legal or compliance teams, especially for lending and hiring use cases.
21
Your cross-validation score is 0.85 but the test score is 0.70. What could explain it?
Possible causes: leakage inside CV, such as preprocessing or feature selection fit outside folds; repeated tuning that overfit the CV folds; a test set from a different time period or population; or duplicate entities across folds but not test. I would check pipeline structure, switch to time-based or group splits that match how test data differs, and compare feature distributions between the two sets.
22
What is a holdout set and why should it be touched only once?
A holdout or test set is kept aside for the final evaluation after all model and hyperparameter choices are done. If you check it repeatedly and adjust the model, you are indirectly tuning on it, so its score becomes optimistic. Use cross-validation or a separate validation set for decisions, and report the holdout score once as the realistic estimate.
23
How would you build a quick baseline before trying complex models?
Use a DummyClassifier or DummyRegressor to see the score of a trivial guess, then a simple model.
from sklearn.dummy import DummyClassifier
base = DummyClassifier(strategy='most_frequent')
base.fit(X_train, y_train)
print(base.score(X_test, y_test))
Any real model must clearly beat this number; otherwise the features carry little signal.
24
What is the difference between batch, stochastic and mini-batch gradient descent?
Batch gradient descent uses the full dataset for each update, giving stable but slow and memory-heavy steps. Stochastic gradient descent updates after every single example, which is fast and noisy and can escape shallow minima. Mini-batch uses small groups such as 32 to 512 rows, balancing stability with speed and allowing vectorised computation. Mini-batch is the default in practice.
from sklearn.ensemble import IsolationForest
iso = IsolationForest(n_estimators=300, contamination=0.01, random_state=42)
iso.fit(X_train)
scores = -iso.score_samples(X_new)
flags = iso.predict(X_new) == -1
Higher scores mean more anomalous. Tune contamination to match how many alerts reviewers can handle.
26
The fraud team can only review 500 alerts a day. How do you set the threshold?
Instead of 0.5, rank transactions by predicted probability and pick the threshold that yields about 500 alerts per day on recent data. Then report precision and recall at that cut-off, or precision at k. I would monitor daily alert volume because drift can change it, and recompute the threshold periodically. If cost data exists, I would choose the threshold that maximises expected savings.
27
Your random forest model file is 2 GB and too slow for real-time scoring. What can you do?
Reduce size by limiting max_depth and min_samples_leaf, using fewer trees since gains flatten after a few hundred, and dropping weak features. Alternatively switch to a gradient boosting model like LightGBM, which usually gives similar accuracy with far smaller, faster models. Converting to ONNX or using a compiled predictor can also speed up inference. Measure accuracy and latency after each change.
28
How do you create polynomial features with regularisation in a pipeline?
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
model = make_pipeline(
PolynomialFeatures(degree=2, include_bias=False),
StandardScaler(),
Ridge(alpha=1.0)
)
model.fit(X_train, y_train)
Tune degree and alpha together with GridSearchCV to avoid overfitting.
29
How do you choose a classification threshold that maximises F1?
from sklearn.metrics import precision_recall_curve
import numpy as np
proba = clf.predict_proba(X_val)[:, 1]
p, r, t = precision_recall_curve(y_val, proba)
f1 = 2 * p * r / (p + r + 1e-9)
best = t[np.argmax(f1[:-1])]
print(best)
Choose it on validation data, not the test set. scikit-learn 1.5+ also offers TunedThresholdClassifierCV.
30
Random forest and LightGBM give the same AUC. Which would you deploy?
I would compare beyond AUC: model size, inference latency, training time, calibration and stability across time periods. LightGBM is usually smaller and faster to score, while random forest needs less tuning and is less prone to overfitting. If both are equal, I would choose the one that is cheaper to run and easier for the team to maintain and retrain.
Tips to crack the Zomato Machine Learning interview
Practise product and business cases about restaurant ratings, delivery times or Blinkit order growth with clear metrics.
Revise SQL and Excel well, since analyst assignments often involve cleaning order data and presenting insights.
Follow Zomato and Eternal shareholder letters, which openly explain business priorities and are useful for interview discussion.
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; Zomato interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Zomato Machine Learning interview?
Candidates usually go through 4 stages: Online assessment or assignment; Technical or case round; Hiring manager round; Culture fit 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 Zomato Machine Learning interview?
This practice set leans on Regression Models (7), Model Evaluation and Tuning (6), ML Fundamentals (5), Decision Trees and Ensembles (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Zomato interviewers ask you to walk through real work.
Are these the exact questions asked at Zomato?
No. These are curated practice questions that match the skills and difficulty usually tested in e-commerce & consumer tech interviews like Zomato's. They are not a leaked or verified list of past questions.
Where does Zomato hire Machine Learning professionals in India?
Zomato has large teams in Gurugram. Openings and locations change often, so check the Zomato careers page and filter by city and experience.