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Machine Learning Telephonic Interview Questions & Answers

30 questions for the telephonic interview stage — 17 easy, 12 medium, 1 hard.

About the telephonic interview round

The telephonic or screening round is a 15-30 minute call with a recruiter or engineer. It checks your basics, your CV and whether you can explain Machine Learning concepts clearly without a screen to point at.

Tips for the telephonic interview round

Keep your CV in front of you and be ready to explain every line on it.
Answer in short, clear sentences; there is no whiteboard, so structure matters.
Take the call somewhere quiet and confirm the time in advance.
Prepare one question to ask the interviewer at the end.

Solved — telephonic interviewStart practicing

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How does logistic regression work?

Classification ModelsEasy

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Logistic regression computes a weighted sum of features and passes it through the sigmoid function, 1 / (1 + e^-z), to get a probability between 0 and 1. It is trained by minimising log loss. Coefficients represent change in log-odds per unit of a feature, and exp(coefficient) gives the odds ratio. A threshold, 0.5 by default, converts probabilities into class labels.

How does k-means clustering work?

Clustering and Dimensionality ReductionEasy

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k-means picks k initial centroids, assigns each point to its nearest centroid, recomputes each centroid as the mean of its points, and repeats until assignments stop changing. It minimises within-cluster sum of squares (inertia). It needs k in advance, scaled features, and assumes roughly spherical, similar-sized clusters. k-means++ initialisation and multiple runs with n_init reduce bad starting points.

How do you choose the number of clusters k?

Clustering and Dimensionality ReductionEasy

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Common methods are the elbow method, plotting inertia against k and looking for the bend; the silhouette score, which measures how close points are to their own cluster versus the next one; and the gap statistic. Business usability matters too: four clear segments a marketing team can act on are better than nine statistically optimal but confusing ones.

What is a model registry and why is it useful?

MLOps and DeploymentMedium

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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.

What are the assumptions of linear regression?

Regression ModelsEasy

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Linear regression assumes a linear relationship between features and target, independent errors, constant error variance (homoscedasticity), normally distributed errors for valid inference, and little multicollinearity among features. Violations mainly hurt confidence intervals and coefficient interpretation more than raw predictions. Check them with residual plots, Q-Q plots, the Durbin-Watson test and VIF.

What is feature engineering and why does it matter?

Data Preprocessing and Feature EngineeringEasy

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Feature engineering is creating input variables that make patterns easier for a model to learn, using domain knowledge. Examples are extracting day of week from a date, ratios like debt-to-income, rolling averages of past purchases, or text length. On tabular problems good features often improve results more than switching algorithms. Features must use only information available at prediction time.

When would you use AdaBoost and how does it re-weight samples?

Decision Trees and EnsemblesMedium

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AdaBoost trains weak learners, usually decision stumps, in sequence. After each round it increases the weights of misclassified samples so the next learner focuses on them, and gives each learner a vote weight based on its accuracy. It works well on clean data but is sensitive to noisy labels and outliers, which get ever larger weights. Gradient boosting is a generalisation that has largely replaced it.

What is k-fold cross-validation and why is it better than a single split?

Model Evaluation and TuningEasy

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k-fold cross-validation splits training data into k folds, trains on k-1 folds and validates on the remaining one, repeating k times so every row is validated once. Averaging the k scores gives a more stable estimate than one random split and shows variance across folds. Five or ten folds are common. Use StratifiedKFold for classification and GroupKFold when rows from one user must stay together.

What is one-hot encoding and when can it cause problems?

Data Preprocessing and Feature EngineeringEasy

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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.

Tell me about a time you had to explain a model's error to a business team.

Regression ModelsMedium

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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.

Compare grid search, random search and Bayesian optimisation.

Model Evaluation and TuningMedium

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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.

What is the difference between Gini impurity and entropy?

Decision Trees and EnsemblesEasy

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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.

A new model has slightly better AUC but worse precision in the top 1,000 cases the team acts on. Which do you pick?

Model Evaluation and TuningMedium

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I would pick the model that does better where decisions are actually made. If the team only reviews the top 1,000 cases, precision at 1,000 or recall at that cut-off matters more than global AUC. I would make that the primary metric in future comparisons, and confirm the result holds across several time periods before switching.

How do you handle missing values in a dataset?

Data Preprocessing and Feature EngineeringEasy

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First understand why values are missing: completely at random, at random, or not at random. Options are dropping rows or columns with heavy missingness, simple imputation with mean, median or mode, model-based imputation like KNNImputer or IterativeImputer, or a separate 'missing' category. Adding a missing-indicator column often helps because missingness itself can be predictive, such as income left blank.

What is the difference between label encoding and ordinal encoding?

Data Preprocessing and Feature EngineeringMedium

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Both map categories to integers. In scikit-learn, LabelEncoder is meant for the target variable, while OrdinalEncoder is for input features. Integer encoding implies an order, so it is fine for truly ordered values like low, medium, high, or for tree models, but it can mislead linear models on nominal data like colours. For nominal features with linear models, prefer one-hot encoding.

Tell me about a time your model did not work as expected. What did you do?

ML FundamentalsMedium

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Situation: my first churn model scored 0.95 AUC in validation but failed in a pilot. Task: find out why. Action: I traced features and found one field updated after customers left, which leaked the label. I removed it, rebuilt features with a strict cut-off date and moved to a time-based split. Result: AUC dropped to 0.78 but held steady in production, and I added a leakage checklist to my workflow.

What is the difference between PCA and t-SNE or UMAP?

Clustering and Dimensionality ReductionHard

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PCA is a linear method that preserves global variance, is fast, deterministic and can transform new data, so it is used for preprocessing. t-SNE and UMAP are non-linear methods that preserve local neighbourhoods, producing good 2D visualisations of clusters. t-SNE distances between clusters are not meaningful. UMAP is faster, keeps more global structure and can transform new points.

A fraud model has 99.6 percent accuracy, but the business says it is useless. Why?

Classification ModelsEasy

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If only 0.4 percent of transactions are fraud, predicting 'not fraud' for everything gives 99.6 percent accuracy and catches nothing. I would switch to recall, precision and PR-AUC, check the confusion matrix, then retrain with class weights or resampling and tune the threshold so the fraud team gets a manageable number of alerts with high recall.

How do you keep learning new ML techniques while working full time?

Classification ModelsEasy

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I set a small weekly routine: one paper summary or library release note, and one hands-on experiment on a public dataset. I follow scikit-learn and LightGBM release notes, and I rebuild ideas on a small problem before suggesting them at work. Recently I tried TunedThresholdClassifierCV and later used it to replace a manual threshold script in our project.

What is the difference between parameters and hyperparameters?

ML FundamentalsEasy

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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.

What is DBSCAN and when is it better than k-means?

Clustering and Dimensionality ReductionMedium

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DBSCAN groups points that lie in dense regions, defined by eps (neighbourhood radius) and min_samples, and labels points in sparse areas as noise. It finds arbitrarily shaped clusters, does not need k, and handles outliers. It struggles when clusters have very different densities and in high dimensions. HDBSCAN improves on it for varying densities.

Why do we split data into training, validation and test sets?

ML FundamentalsEasy

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The training set fits model parameters, the validation set is used to choose hyperparameters and compare models, and the test set gives a final unbiased estimate of performance. If you tune on the test set, its score becomes optimistic because information has leaked into model choices. A common split is 70/15/15, or train/test plus cross-validation on the training part.

Tell me about a time you discovered your own mistake after sharing results.

Model Evaluation and TuningMedium

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Situation: after presenting a 12 percent uplift from a pricing model, I found my evaluation used a random split on time-series data. Task: correct it quickly. Action: I re-ran with a time-based split, which showed 7 percent, and informed my manager the same day with the corrected analysis. Result: the team appreciated the honesty, the rollout plan used the right number, and I added split checks to our review template.

What are overfitting and underfitting, and how do you detect them?

ML FundamentalsEasy

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Overfitting is when a model memorises training noise and performs much worse on unseen data. Underfitting is when the model is too simple to capture the pattern, so both training and validation scores are poor. Detect them by comparing training and validation metrics or plotting learning curves. A large gap means overfitting; two low, close scores mean underfitting.

What is PCA and how does it work?

Clustering and Dimensionality ReductionMedium

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Principal Component Analysis finds new orthogonal axes, the principal components, that capture maximum variance in the data. It works by computing eigenvectors of the covariance matrix, or via SVD, after centring and usually standardising the data. Keeping the top components that explain, say, 95 percent of variance reduces dimensions, removes correlation and speeds up models, but the components are harder to interpret.

What is the curse of dimensionality?

ML FundamentalsMedium

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As the number of features grows, data becomes sparse in the feature space, distances between points become similar, and the amount of data needed to generalise grows rapidly. Distance-based methods like k-NN and k-means suffer most. Remedies include feature selection, dimensionality reduction such as PCA, regularisation and collecting more data. Text and genomics data often face this problem.

What is a holdout set and why should it be touched only once?

Model Evaluation and TuningEasy

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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.

What is R-squared and what is adjusted R-squared?

Regression ModelsEasy

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R-squared is the share of variance in the target explained by the model, 1 - SS_res / SS_tot. It never decreases when you add features, even useless ones. Adjusted R-squared penalises extra features: 1 - (1 - R2)(n - 1)/(n - p - 1), so it only rises if a new feature improves fit more than expected by chance. R-squared can be negative on test data if the model is worse than predicting the mean.

What is the difference between batch, stochastic and mini-batch gradient descent?

ML FundamentalsMedium

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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.

What is Naive Bayes and why is it called naive?

Classification ModelsEasy

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Naive Bayes applies Bayes' theorem to compute the probability of each class given the features, assuming features are conditionally independent given the class. That independence assumption is rarely true, hence 'naive', but the model still works well for text classification like spam detection. Variants include Gaussian, Multinomial for word counts and Bernoulli for binary features. It is fast and needs little data.

Frequently asked questions

What happens in the Machine Learning telephonic interview round?

How do I clear the telephonic interview round?

Keep your CV in front of you and be ready to explain every line on it. Answer in short, clear sentences; there is no whiteboard, so structure matters. Take the call somewhere quiet and confirm the time in advance.

Which round comes next?

A typical order is online assessment, telephonic screening, one or two technical rounds, a managerial round and HR. Use the other round pages to practise each stage.

Practise every interview round

Online Assessment questions Technical Round questions Managerial Round questions Rapid-Fire questions HR & behavioural questions Full mock interview

More ways to practise

Company-wise Machine Learning questions Experience-wise Machine Learning questions Role-wise Machine Learning questions City-wise Machine Learning questions Most asked Machine Learning questions Scenario-based questions Practical / hands-on questions HR & behavioural questions Machine Learning mock interview Online assessment questions Technical round questions Managerial round questions Machine Learning topic sheets