Top 18 Data Science GEN AI Real Interview Questions Asked In Microsoft

Machine Learning interviews often span from conceptual understanding to hands-on coding implementations. Whether you’re just starting out or already working in data science, having a strong command of ML fundamentals, algorithms, and practical applications is key to cracking your next interview.

In this blog post, I’ll walk you through 18 essential ML questions — divided into easy, medium, hard, and practical code-based categories, complete with code snippets, visual intuition, and real-world applications.

🟢 Easy Level Questions

1. What is the Bias-Variance Tradeoff in data Science?

In supervised learning, the bias-variance tradeoff is the tension between two types of errors:

Bias: Assumptions made by the model to simplify the learning process. High bias can cause underfitting.
Variance: Sensitivity to fluctuations in training data. High variance can lead to overfitting.

A model with low bias and low variance is ideal, but in reality, improving one often worsens the other.

📊 Example:

High Bias: Linear regression on non-linear data
High Variance: Deep decision tree on small dataset

Visualization:

Model Complexity	Bias	Variance	Total Error
Low	High	Low	High
Medium	Medium	Medium	Low
High	Low	High	High

Takeaway: Choose algorithms and hyperparameters that balance both.

2. Difference Between Parametric and Non-Parametric Models

Aspect	Parametric Models	Non-Parametric Models
Assumption	Predefined form (e.g., linear)	No fixed structure
Parameters	Fixed number (e.g., weights)	Grows with data
Flexibility	Less flexible	Highly flexible
Interpretability	Easy to interpret	Hard to interpret
Examples	Linear Regression, Logistic Reg.	KNN, Decision Trees, SVM

Use Case:

Use parametric when data is small, noise-free, and assumptions hold.
Use non-parametric when data is large or complex.

3. What is the Central Limit Theorem (CLT)?

The Central Limit Theorem states that the sampling distribution of the sample mean of a large enough number of independent samples approaches a normal distribution, regardless of the shape of the original distribution.

📌 Why it matters:
CLT justifies using normality-based statistical tests (like z-tests, t-tests) even if the data isn’t normally distributed.

Real-World Use:

Quality control in manufacturing
A/B testing in marketing

4. What is Cross-Validation?

Cross-validation is a resampling technique used to evaluate machine learning models on a limited dataset.

🧠 K-Fold Cross Validation:

Divide dataset into k parts (folds)
Train on k-1, test on 1
Repeat for each fold
Average results

📦 Why use it?

Helps prevent overfitting
Gives more stable estimate of model performance

Code (Sklearn):

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
model = LogisticRegression()
scores = cross_val_score(model, X, y, cv=5)
print("Accuracy: ", scores.mean())

🟡 Medium Level Questions

5. What is a p-value?

A p-value is the probability that the observed result occurred by chance, assuming the null hypothesis is true.

p < 0.05: Statistically significant (reject null hypothesis)
p > 0.05: Not statistically significant

📈 Use Case in ML:
In feature selection (e.g., backward elimination), features with high p-values can be dropped.

6. What is Multicollinearity? How can it be detected?

Multicollinearity occurs when independent variables in a regression model are highly correlated, making coefficient estimates unstable.

🔍 How to detect:

Correlation Matrix
VIF (Variance Inflation Factor): VIF > 10 is problematic.

Code:

from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd

X = pd.DataFrame(...)  # feature matrix
vif = pd.DataFrame()
vif["VIF"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
vif["Feature"] = X.columns

7. What is the K-Means Clustering Algorithm?

K-Means is an unsupervised learning algorithm that partitions data into K clusters.

🧮 Steps:

Initialize K centroids randomly
Assign data points to nearest centroid
Recompute centroids
Repeat until convergence

📌 Application:

Customer segmentation
Image compression
Anomaly detection

8. How Does the Random Forest Algorithm Work?

Random Forest is a bagging-based ensemble method that trains multiple decision trees on different subsets of data and features.

🌲 Key Concepts:

Each tree votes; the final prediction is majority vote (classification) or average (regression)
Helps reduce overfitting compared to single decision trees

🧠 When to Use:

Works well with missing values
Robust to outliers
Feature importance extraction

9. Explain Gradient Boosting

Gradient Boosting builds trees sequentially, where each new tree learns from the errors of the previous trees by minimizing a loss function using gradient descent.

🔥 Popular Frameworks:

XGBoost: Highly optimized, parallel
LightGBM: Fast, low memory usage
CatBoost: Categorical handling

📌 When to use:

Structured/tabular data
Winning competitions (Kaggle, Zindi)

10. What is Principal Component Analysis (PCA)?

PCA is a dimensionality reduction technique that transforms features into new variables (principal components) that capture the maximum variance.

🧠 Steps:

Standardize the data
Compute covariance matrix
Calculate eigenvalues/vectors
Project data onto top N components

📦 Use Case:

Speed up ML models
Remove multicollinearity
Data visualization (2D/3D)

🔴 Hard Level Questions

11. Difference Between Bagging and Boosting

Factor	Bagging	Boosting
Strategy	Train models in parallel	Train models sequentially
Objective	Reduce variance	Reduce bias
Example Models	Random Forest	AdaBoost, XGBoost, LightGBM
Data Sampling	Bootstrapped datasets	Weighted data (focus on hard examples)
Overfitting	Less prone	More prone (unless regularized)

🧠 When to Use:

Use bagging when the model is overfitting (high variance)
Use boosting when the model is underfitting (high bias)

12. Generative vs. Discriminative Models

Type	Generative	Discriminative
Learns	Joint distribution P(X,Y)	Conditional distribution P(Y
Usage	Can generate new data	Best for classification
Examples	Naive Bayes, GANs, HMM	Logistic Regression, SVM, XGBoost

13. What is the Vanishing Gradient Problem?

In deep neural networks, gradients used in backpropagation become extremely small as they move toward the input layers, making learning very slow.

📌 Occurs mostly with:

Sigmoid/Tanh activation
Deep RNNs

🛠 Solutions:

Use ReLU
Batch Normalization
Residual connections (ResNet)
LSTM/GRU for RNNs

14. What are RNNs and Their Variants?

Recurrent Neural Networks (RNNs) are specialized for sequential data (text, time series).

🔄 They maintain a hidden state that captures previous inputs, enabling context learning.

📦 Variants:

LSTM: Solves vanishing gradient, long memory
GRU: Simpler than LSTM, performs similarly

📌 Use Cases:

Text generation
Language modeling
Time series forecasting

15. What is a Support Vector Machine (SVM)?

SVM is a supervised learning model that finds the optimal hyperplane to separate different classes with maximum margin.

🧠 Key Concepts:

Supports linear and non-linear classification via kernels (RBF, Polynomial)
Effective in high-dimensional space

📌 Use Cases:

Image recognition
Text classification
Bioinformatics

👨‍💻 Practical Code-Based Questions

16. Python Function for Logistic Regression using Gradient Descent

import numpy as np

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

def logistic_regression(X, y, lr=0.01, epochs=1000):
    m, n = X.shape
    weights = np.zeros(n)
    for _ in range(epochs):
        predictions = sigmoid(np.dot(X, weights))
        error = predictions - y
        gradient = np.dot(X.T, error) / m
        weights -= lr * gradient
    return weights

17. Implement Neural Network From Scratch

import numpy as np

def sigmoid(x): return 1 / (1 + np.exp(-x))
def sigmoid_deriv(x): return x * (1 - x)

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

input_dim, hidden_dim, output_dim = 2, 4, 1
w0 = np.random.randn(input_dim, hidden_dim)
w1 = np.random.randn(hidden_dim, output_dim)

for epoch in range(10000):
    l1 = sigmoid(np.dot(X, w0))
    l2 = sigmoid(np.dot(l1, w1))
    l2_error = y - l2
    l2_delta = l2_error * sigmoid_deriv(l2)
    l1_error = l2_delta.dot(w1.T)
    l1_delta = l1_error * sigmoid_deriv(l1)

    w1 += l1.T.dot(l2_delta)
    w0 += X.T.dot(l1_delta)

18. Naive Bayes Classifier From Scratch

Read Full Theory here https://en.wikipedia.org/wiki/Naive_Bayes_classifier

import numpy as np
from collections import defaultdict

class NaiveBayes:
    def fit(self, X, y):
        self.classes = np.unique(y)
        self.stats = {}
        for c in self.classes:
            X_c = X[y == c]
            self.stats[c] = {
                "mean": np.mean(X_c, axis=0),
                "var": np.var(X_c, axis=0),
                "prior": len(X_c) / len(X)
            }

    def predict(self, X):
        def gaussian(x, mean, var):
            return (1 / np.sqrt(2 * np.pi * var)) * np.exp(-((x - mean) ** 2) / (2 * var))

        predictions = []
        for x in X:
            probs = {}
            for c in self.classes:
                prior = np.log(self.stats[c]["prior"])
                likelihood = np.sum(np.log(gaussian(x, self.stats[c]["mean"], self.stats[c]["var"])))
                probs[c] = prior + likelihood
            predictions.append(max(probs, key=probs.get))
        return predictions

🚀 Conclusion

Cracking ML interviews requires a mix of conceptual clarity and coding ability. These 18 questions cover all aspects — from theoretical concepts like bias-variance tradeoff to practical implementation like logistic regression and neural nets.

✅ Tip: Don’t just memorize — understand the intuition, practice the code, and apply them in mini-projects or datasets.

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