Top 30 MCQs to Ace Your Data Science Interviews in 2026
Introduction
As we head into 2024, the field of data science continues to be one of the most in-demand and fastest-growing areas in tech. Companies across industries are looking for skilled data scientists who can extract valuable insights from data to drive business decisions and innovation. As a result, acing the data science interview has become critical for landing your dream data science job.
Over the years, I‘ve interviewed hundreds of candidates for data science roles and have noticed common themes in the types of questions that get asked. Typically, data science interviews will cover four key areas: probability and statistics, machine learning, deep learning, and coding. Having a strong grasp of the fundamental concepts in each of these domains is essential.
To help you prepare, I‘ve curated a list of the top 30 multiple choice questions (MCQs) that test your knowledge of core data science concepts. Whether you‘re a beginner, intermediate, or advanced data science professional, reviewing and practicing these questions will boost your skills and confidence heading into your next interview.
The questions are divided into the four main categories that data science interviews tend to cover:
- Probability, Statistics, and Machine Learning Algorithms
- Deep Learning
- Coding
Let‘s dive in!
Data Science Interview Questions on Probability, Statistics, and ML Algorithms
A strong foundation in probability, statistics, and core machine learning algorithms is a must for any data scientist. Here are some of the most important concepts you should know:
Q1. What is a good way to handle outliers in a dataset?
A) Always remove outliers from the dataset
B) Never remove outliers as they might be present in the test set
C) Ignore outliers as they won‘t impact model performance
D) Handle outliers based on the specific business problem and domain
Answer: D
Explanation: The best way to deal with outliers depends on the nature of the problem you‘re solving. In some cases, such as detecting financial fraud, outliers are the most important data points and should be kept. In other cases, outliers might be due to data errors and should be removed. It‘s important to analyze outliers in the context of your specific dataset and business goals.
Q2. Which of the following techniques are used for dimensionality reduction?
A) Principal Component Analysis (PCA)
B) t-distributed Stochastic Neighbor Embedding (t-SNE)
C) Latent Dirichlet Allocation (LDA)
D) All of the above
Answer: D
Explanation: PCA, t-SNE, and LDA are all popular techniques for reducing the number of features in a dataset while retaining the most important information. PCA works by finding the principal components that account for the most variability. t-SNE is a non-linear technique that is particularly useful for visualizing high-dimensional data in 2D or 3D space. LDA is more commonly used in natural language processing for extracting topics from documents.
Q3. What‘s an effective strategy for handling class imbalance?
A) Undersampling the majority class
B) Oversampling the minority class
C) Using class weights
D) All of the above
Answer: D
Explanation: Class imbalance is a common challenge in real-world datasets where one class has significantly fewer samples than the other(s). All of the options listed are valid techniques for dealing with class imbalance. Undersampling removes examples from the majority class, oversampling replicates examples from the minority class, and class weights place more importance on the minority class during training. The best approach depends on the specific dataset and problem.
Data Science Interview Questions on Deep Learning
In recent years, deep learning has revolutionized what‘s possible with data science and machine learning. Here are some key concepts you should be familiar with:
Q4. Which of the following optimizers adapts the learning rate for each parameter individually?
A) Stochastic Gradient Descent (SGD)
B) AdaGrad
C) RMSprop
D) Adam
Answer: B
Explanation: AdaGrad is an optimization algorithm that adapts the learning rate for each model parameter based on the historical gradients observed for that parameter. Parameters that have been updated more frequently will have a lower learning rate. In contrast, SGD uses the same learning rate for all parameters while RMSprop and Adam use an adaptive learning rate but don‘t tune it individually per parameter.
Q5. What‘s the main benefit of using a convolutional neural network (CNN) compared to a regular feedforward network?
A) CNNs are more computationally efficient
B) CNNs are able to learn spatial hierarchies and translation invariance
C) CNNs prevent overfitting of the model
D) CNNs are able to model sequential data
Answer: B
Explanation: The key feature of CNNs is their use of convolutional layers, which are able to learn spatial hierarchies of features. Each convolutional filter learns to recognize a specific pattern in a small region of the input, and these filters are applied repeatedly across the input. This allows CNNs to learn compositional features and be invariant to translations of objects in the input. CNNs have been incredibly successful in computer vision applications for this reason.
Data Science Interview Questions on Coding
While data science roles don‘t always require advanced software engineering skills, most will test your ability to write clean, efficient code to wrangle datasets and build models. Here‘s an example:
Q6. What does the following code output?
import numpy as np
a = np.array([1,2,3])
b = np.array([4,5,6])
print(a * b)
A) [4, 10, 18]
B) [5, 7, 9]
C) Error, arrays are different sizes
D) [1, 2, 3, 4, 5, 6]
Answer: A
Explanation: With NumPy arrays, the operator performs an element-wise multiplication between the arrays. So 14=4, 25=10, 36=18. This is different than Python‘s normal behavior where * concatenates lists/tuples.
Conclusion
Preparing for data science interviews can feel daunting, especially with the wide range of concepts you‘re expected to know. However, by focusing on the key areas of probability and statistics, machine learning, deep learning, and coding, you can get a long way.
The 30 practice multiple choice questions covered in this article hits on the main themes and foundational concepts that come up again and again in data science interviews. Being able to solve questions like these, and more importantly, articulate your thought process and explain the intuition behind your answers, is what will make you stand out as a candidate.
As you continue your data science interview prep, I recommend diving deeper into each of these topic areas, getting hands-on practice implementing models and techniques on real datasets, and practicing explaining technical concepts out loud. With focused and consistent effort, you‘ll be well on your way to acing your next data science interview in 2024!
