Skip to content
Machine Learning
All coursesPlacement Report
‹ Back to previous sectionCourse homeAll page URLs

Data Preprocessing and Feature Engineering Interview Questions for Machine Learning

25 Data Preprocessing and Feature Engineering questions with answers for machine learning roles — 8 easy, 14 medium, 3 hard.

About Data Preprocessing and Feature Engineering

Real data is messy, and most of an ML engineer's time goes into cleaning and shaping it. This topic covers missing values, outliers, scaling, categorical encoding, target encoding, feature selection, multicollinearity and data leakage, plus building leak-proof scikit-learn pipelines with ColumnTransformer so the same preprocessing runs in training and production.

Sheet progressStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
3 questions free Unlock the rest in seconds
01

Why is feature scaling needed and which algorithms need it?

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
Scaling puts features on comparable ranges so that one large-valued feature does not dominate distances or gradients. Distance and gradient based models need it: k-NN, k-means, SVM, PCA, linear and logistic regression with regularisation, and neural networks. Tree-based models like random forest and XGBoost split on thresholds, so they are largely unaffected by scaling.
02

What is the difference between standardisation and normalisation?

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
Standardisation (StandardScaler) transforms a feature to mean 0 and standard deviation 1 using (x - mean) / std; it keeps outliers but does not bound values. Min-max normalisation (MinMaxScaler) rescales to a fixed range such as 0 to 1 and is sensitive to outliers. RobustScaler uses median and IQR and is better when outliers are present. Always fit the scaler on training data only.
03

How do you handle missing values in a dataset?

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
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.
04

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

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
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.
05

What is feature engineering and why does it matter?

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
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.
06

How do you extract useful features from a datetime column in pandas?

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
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.
07

How do you find and remove duplicate rows and check missing values in pandas?

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
Quick data-quality checks before modelling: print(df.isna().sum().sort_values(ascending=False)) print(df.duplicated().sum()) df = df.drop_duplicates() df['age'] = df['age'].fillna(df['age'].median()) For modelling, do the median imputation inside a pipeline instead so the value is learned only from training data.
08

How do you explain data cleaning decisions to a non-technical manager?

Data Preprocessing and Feature EngineeringEasy
Save to ▾
Show answer
I avoid jargon and tie each decision to its effect on business results. For example: 'About 8 percent of orders had missing delivery times. Instead of deleting them, which would hide slow regions, I filled them using similar orders and flagged them, so the model still learns from those regions.' I keep a short log of decisions so they can be reviewed later and repeated.
09

What is the difference between label encoding and ordinal encoding?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
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.
10

What is data leakage? Give two examples.

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
Data leakage is when information not available at prediction time gets into training, giving falsely high validation scores. Example one: fitting a scaler or imputer on the full dataset before splitting. Example two: using a feature created after the event, such as 'refund issued' when predicting fraud. Prevent it with pipelines, time-aware splits and checking each feature's timestamp.
11

How do you detect and treat outliers?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
Detect with box plots, the IQR rule (below Q1 - 1.5 IQR or above Q3 + 1.5 IQR), z-scores above 3, or models like Isolation Forest. Treatment depends on cause: fix data entry errors, cap values (winsorise), apply log transforms for skewed data, or use robust models and losses. Genuine extreme values, such as large fraud amounts, may be the most important signal and should not be removed blindly.
12

Compare filter, wrapper and embedded feature selection methods.

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
Filter methods score features independently of a model using statistics such as correlation, chi-square or mutual information; they are fast but ignore interactions. Wrapper methods like Recursive Feature Elimination train models on feature subsets; they are accurate but slow. Embedded methods select during training, such as L1 regularisation or tree feature importances. Embedded methods are a common practical middle ground.
13

What is multicollinearity and how do you detect it?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
Multicollinearity is high correlation between input features, which makes linear model coefficients unstable and hard to interpret, though predictions may remain fine. Detect it with a correlation matrix or the Variance Inflation Factor, where VIF above 5 to 10 signals a problem. Fix it by dropping one of the correlated features, combining them, using PCA, or applying Ridge regularisation.
14

Why do skewed features often need a log or power transform?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
Heavily right-skewed features like income or transaction amount have long tails that can dominate linear models and distance metrics. A log1p, Box-Cox or Yeo-Johnson transform compresses large values and makes the distribution closer to normal, which often improves linear and distance-based models. Yeo-Johnson works with zero and negative values. Tree models usually do not need these transforms.
15

Your dataset has a city column with 2,000 unique values. How will you encode it?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
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.
16

Validation AUC is 0.99 on a loan default model. What do you suspect?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
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.
17

40 percent of values in an important income column are missing. What do you do?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
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.
18

How do you build a scikit-learn pipeline for mixed numeric and categorical data?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
Use ColumnTransformer inside a Pipeline so preprocessing is fit only on training folds. from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder num = Pipeline([('imp', SimpleImputer(strategy='median')), ('sc', StandardScaler())]) pre = ColumnTransformer([ ('num', num, num_cols), ('cat', OneHotEncoder(handle_unknown='ignore'), cat_cols) ]) model = Pipeline([('pre', pre), ('clf', LogisticRegression(max_iter=1000))])
19

How do you calculate VIF for features in Python?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
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.
20

How do you select the top k features using mutual information in scikit-learn?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
SelectKBest with mutual_info_classif captures non-linear relationships, unlike plain correlation. from sklearn.feature_selection import SelectKBest, mutual_info_classif sel = SelectKBest(score_func=mutual_info_classif, k=20) X_new = sel.fit_transform(X_train, y_train) print(X_train.columns[sel.get_support()]) Put the selector inside a pipeline so selection is redone in each CV fold.
21

Describe a time you found a serious data quality issue late in a project.

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
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.
22

How would you encode cyclical features like hour of day or month?

Data Preprocessing and Feature EngineeringMedium
Save to ▾
Show answer
Map the value onto a circle with sine and cosine so the end connects to the start. import numpy as np df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24) df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24) Now 23:00 and 00:00 are close, which linear and distance-based models cannot see with raw integers. Tree models often manage with the raw value.
23

What is target encoding and how do you avoid leakage with it?

Data Preprocessing and Feature EngineeringHard
Save to ▾
Show answer
Target encoding replaces each category with the mean target value for that category, which works well for high-cardinality features. It leaks if computed on the full data because each row sees its own label. Avoid this by computing encodings out-of-fold within cross-validation and adding smoothing toward the global mean for rare categories. scikit-learn 1.3+ has TargetEncoder with built-in cross-fitting.
24

New categories appear in production that were not in training data. How do you handle it?

Data Preprocessing and Feature EngineeringHard
Save to ▾
Show answer
Make the encoder tolerant: OneHotEncoder with handle_unknown='ignore' or infrequent_if_exist, or map unseen values to an 'Other' bucket. Monitor the share of unseen categories as a drift signal. If new categories become common, such as a new city launch, retrain with fresh data. Tests in the inference pipeline should include unseen values so the service never crashes.
25

Your preprocessing works in notebooks but gives different results in production. Why?

Data Preprocessing and Feature EngineeringHard
Save to ▾
Show answer
Common causes are preprocessing written separately in two places, statistics like means recomputed on production data instead of using training values, different library versions, or different column order and data types. The fix is to save one fitted Pipeline with preprocessing and model together, pin versions, validate input schema, and test that the saved pipeline gives identical outputs on sample rows.

Frequently asked questions

How important is Data Preprocessing and Feature Engineering in Machine Learning interviews?

Real data is messy, and most of an ML engineer's time goes into cleaning and shaping it. This topic covers missing values, outliers, scaling, categorical encoding, target encoding, feature selection, multicollinearity and data leakage, plus building leak-proof scikit-learn pipelines with ColumnTransformer so the same preprocessing runs in training and production.

How should I use the Data Preprocessing and Feature Engineering sheet?

Go top to bottom — easy to hard. Answer before opening each answer, tick what you solved and save misses to Last-Minute Revision.

More question sheets

ML Fundamentals questionsRegression Models questionsClassification Models questionsDecision Trees and Ensembles questionsClustering and Dimensionality Reduction questionsModel Evaluation and Tuning questionsMLOps and Deployment questions