36 practice questions with answers for Machine Learning interviews in Delhi.
Machine Learning jobs in Delhi
Delhi NCR has one of India's biggest pools of IT, consulting, banking and start-up jobs. Delhi itself hosts many corporate, government-tech and consulting offices, and most candidates also apply across Noida and Gurgaon, so interviews here mix service-company and product-company styles.
Below are 36 questions that match the mix of service, product and domain employers hiring machine learning engineers in Delhi.
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Bias is error from overly simple assumptions, which causes underfitting. Variance is error from sensitivity to the particular training sample, which causes overfitting. Increasing model complexity usually lowers bias but raises variance. The goal is the complexity that minimises total test error. For example, a linear model on curved data has high bias, while a very deep unpruned tree has high variance.
02
What is polynomial regression and what risk does it carry?
Polynomial regression adds powers and interaction terms of features, such as x squared and x1 times x2, so a linear model can fit curves. It is still linear in its coefficients. The main risk is overfitting at high degrees, plus wild behaviour outside the training range. Use cross-validation to choose the degree, scale features, and combine it with Ridge regularisation.
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.
04
What is the difference between parameters and hyperparameters?
Parameters are learned from data during training, such as the weights of a linear regression or split thresholds of a tree. Hyperparameters are set before training and control the learning process, such as learning rate, max_depth, number of trees or regularisation strength C. Hyperparameters are tuned with validation data using grid search, random search or Bayesian optimisation.
05
Your dataset has a city column with 2,000 unique values. How will you encode it?
One-hot would create 2,000 sparse columns, so I would first group rare cities below a frequency threshold into 'Other'. Then options are frequency encoding, out-of-fold target encoding, or mapping cities to useful attributes such as tier, state or population. For boosting models like CatBoost or LightGBM, native categorical handling also works well. I would compare options with cross-validation.
First define churn precisely, for example no purchase in 60 days, and the prediction window. It becomes binary classification. Build features from data available before the cut-off date only, choose a time-based split, and pick metrics like recall at a fixed precision or lift in the top decile. Start with logistic regression as a baseline, then try boosting, and agree on how retention teams will use the scores.
09
Tell me about a time your analysis was not used by stakeholders. What did you learn?
Situation: I built eight customer segments, but marketing did not use them. Task: understand why. Action: I met the team and learned the segments were hard to act on and lacked clear names. I reduced them to five, added a one-page profile and suggested campaign ideas for each. Result: two segments were used in the next campaign. I learned to involve users from the start.
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.
I would first check data pipelines for broken features, nulls or schema changes, then compare current feature distributions with training data using PSI to find drift. I would check whether the label definition or business process changed, and analyse errors by segment. If drift is real, retrain on recent data, possibly with time-weighted samples, and set up automatic drift alerts and scheduled retraining.
13
Tell me about a time you had to explain a model's error to a business team.
Situation: a sales forecast missed by 18 percent in one month. Task: explain why to the category team. Action: I broke the error down by region and found most came from a sudden promotion not in our data. I showed a simple chart and proposed adding a promotion calendar feed. Result: the team shared promotion plans in advance, and the next quarter's error fell below 8 percent.
14
Compare grid search, random search and Bayesian optimisation.
Grid search tries every combination of listed values; it is thorough but expensive and wastes trials on unimportant parameters. Random search samples combinations and usually finds good settings faster in high-dimensional spaces. Bayesian optimisation, as in Optuna or Hyperopt, builds a model of past trials to pick promising settings next, using fewer trials. Successive halving methods add early pruning of weak trials.
Start with the right metrics, such as PR-AUC, recall at fixed precision or F1, not accuracy. Then use class_weight='balanced', resampling like random undersampling or SMOTE applied only on training folds, and threshold tuning based on business cost. Boosting models with scale_pos_weight also help. Stratified splits keep class ratios consistent across folds.
16
How do you build a text classifier with TF-IDF and logistic regression?
Convert to datetime and pull out calendar parts.
import pandas as pd
df['ts'] = pd.to_datetime(df['ts'])
df['hour'] = df['ts'].dt.hour
df['dow'] = df['ts'].dt.dayofweek
df['month'] = df['ts'].dt.month
df['is_weekend'] = df['dow'].isin([5, 6]).astype(int)
For cyclical features like hour, sine and cosine encoding keeps 23 and 0 close together.
Gini impurity is 1 - sum of p squared and measures the chance of misclassifying a random sample. Entropy is - sum of p log p and comes from information theory; the reduction is called information gain. Both usually produce very similar trees. Gini is slightly faster because it avoids logarithms, which is why it is the scikit-learn default.
20
What should you monitor for a model in production?
Monitor service health such as latency, error rate and throughput; input data quality such as missing values, schema changes and out-of-range values; feature and prediction distribution drift; and real performance once labels arrive. Also track business KPIs the model influences. Set alerts with thresholds and owners, and keep a dashboard so issues are found before users report them.
21
A product team wants 'similar products' recommendations with no purchase labels. How would you start?
I would build product embeddings from content: TF-IDF or sentence embeddings of titles and descriptions, plus category and price features. Then use cosine similarity with nearest-neighbour search, such as scikit-learn NearestNeighbors or an approximate index like FAISS for large catalogues. I would review samples with the product team and later add co-view or co-purchase signals once collected.
22
What is boosting and how is it different from bagging?
Boosting trains models sequentially, each one focusing on the errors of the combined previous models, which mainly reduces bias. Bagging trains models independently in parallel and averages them, which mainly reduces variance. Boosting uses shallow trees and a learning rate, and can overfit if run too long, so early stopping is important. Examples are AdaBoost, Gradient Boosting, XGBoost, LightGBM and CatBoost.
23
What is the no free lunch theorem and what does it mean in practice?
It states that no single algorithm is best for every problem when averaged over all possible problems. In practice it means you should try several model families, start with a simple baseline, and choose based on validation results for your data. For tabular data gradient boosting often wins, but a logistic regression may be preferred if interpretability matters more.
24
What is probability calibration and why does it matter?
A calibrated model's predicted probabilities match real frequencies: of cases scored 0.8, about 80 percent should be positive. It matters when scores drive decisions like credit limits or expected loss. Random forests and SVMs are often poorly calibrated. Fix it with CalibratedClassifierCV using Platt scaling (sigmoid) or isotonic regression, and check with a reliability curve or Brier score.
25
What is association rule mining? Explain support, confidence and lift.
Association rule mining finds items that occur together, such as in market basket analysis. Support is how often an itemset appears in all transactions. Confidence of A to B is the share of transactions with A that also contain B. Lift is confidence divided by B's support; lift above 1 means A and B occur together more than by chance. Apriori and FP-Growth are common algorithms.
Use statsmodels on the numeric feature matrix with a constant added.
import pandas as pd
from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.tools import add_constant
Xc = add_constant(X_num)
vif = pd.Series([variance_inflation_factor(Xc.values, i)
for i in range(Xc.shape[1])], index=Xc.columns)
print(vif.drop('const'))
Values above 5 to 10 indicate strong multicollinearity.
27
How do you check regression residuals visually in Python?
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.
29
Describe a time you found a serious data quality issue late in a project.
Situation: two days before a demo, I noticed sales for one region were doubled. Task: fix it without missing the deadline. Action: I traced it to a join that duplicated rows on a non-unique key, fixed the join, added row-count assertions to the pipeline and retrained. I informed my lead immediately with the impact. Result: the demo went ahead with correct numbers, and the assertions caught a similar issue later.
30
How do one-vs-rest and multinomial approaches differ for multiclass classification?
One-vs-rest trains one binary classifier per class against all others and picks the highest score; it is simple but probabilities may not sum to one. Multinomial (softmax) logistic regression models all classes jointly with a single loss, giving consistent probabilities. For metrics, use macro averaging to treat classes equally or weighted averaging to account for class frequency.
Isolation Forest builds random trees that split on random features and random thresholds. Anomalies are few and different, so they get isolated in fewer splits, giving shorter average path lengths. The anomaly score comes from that path length. It is fast, works without labels and scales well. The contamination parameter sets the expected share of anomalies and determines the threshold.
32
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.
33
What is one-hot encoding and when can it cause problems?
One-hot encoding turns a categorical column into binary columns, one per category, so models do not assume order. It causes problems with high-cardinality features like city or product ID, creating thousands of sparse columns and higher memory and overfitting risk. Alternatives are target encoding, frequency encoding, hashing, or grouping rare categories into 'Other'. Use handle_unknown='ignore' for unseen categories.
34
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.
35
Your team retrains models manually in notebooks every month. How would you improve it?
I would convert the notebook into a versioned training pipeline with separate steps for data extraction, validation, feature building, training and evaluation, scheduled with Airflow or a cloud pipeline. Each run would log to MLflow, compare against the production model on a fixed holdout, and register the new model only if it passes thresholds. Promotion would stay a reviewed step, at least initially.
36
Validation AUC is 0.99 on a loan default model. What do you suspect?
Such a high score on a hard problem usually signals leakage. I would check feature importances for a suspiciously dominant feature, look for fields recorded after the loan outcome such as collection status, verify preprocessing was fit only on training data, and check for duplicate customers across train and test. Then re-run with a time-based split to confirm a realistic score.
Frequently asked questions
Which companies hire Machine Learning professionals in Delhi?
Companies with large offices in Delhi include many IT services and product firms. Use their company pages below to practise employer-specific sets.
Are Machine Learning interviews in Delhi different from other cities?
The questions depend more on the company than the city. Delhi's mix of employers decides the style: Delhi itself hosts many corporate, government-tech and consulting offices, and most candidates also apply across Noida and Gurgaon, so interviews here mix service-company and product-company styles
How do I find Machine Learning jobs in Delhi?
Shortlist companies with Delhi offices, keep your CV focused on Machine Learning projects, apply on company career pages and job portals with the Delhi filter, and practise the questions on this page before each interview.