Master Your Interview
Top 40 NumPy Interview Questions
Entry Junior Mid Senior Expert
NumPy Theoretical Questions
Q1:
Why to use NumPy?
Entry
Q2:
Explain what is ndarray in NumPy
Junior
Q3:
Compute the min/max for each row for a NumPy 2D array
min/max for each row for a NumPy 2D array Junior
Q4:
How would you convert a Pandas DataFrame into a NumPy array?
Junior
Q5:
What is the difference between ndarray and array in NumPy?
ndarray and array in NumPy? Junior
Mid Q7:
Explain what is Vectorization in NumPy
Mid Q8:
What does einsum do in NumPy?
einsum do in NumPy? Mid Q9:
What is the difference between Vectorisation vs Broadcasting in NumPy?
Mid Mid Q11:
What are the advantages of NumPy over regular Python lists?
Mid Q12:
What are the differences between np.mean() vs np.average() in Python NumPy?
np.mean() vs np.average() in Python NumPy? Mid Q13:
What is the most efficient way to map a function over a NumPy array?
Mid Q14:
What are the differences between NumPy arrays and matrices?
Mid Q15:
What is the difference between flatten and ravel functions in NumPy?
flatten and ravel functions in NumPy? Mid Mid Q17:
What is the difference between contiguous and non-contiguous arrays?
Senior Q18:
What is the difference between the following assignment methods?
Senior Q19:
What are Strides in NumPy? How does it work?
Senior Q20:
What's the difference between a View and a Shallow Copy of a NumPy array?
Senior Q21:
How to normalize an array in NumPy to a Unit Vector?
Senior Q22:
What are some main reason why NumPy is so fast?
Senior Q23:
What is Fortran contiguous arrays?
Expert NumPy Practical Challenges
Q1:
How to access the i-th column of a NumPy multidimensional array?
i-th column of a NumPy multidimensional array?
Junior Junior Q3:
Calculate the Euclidean Distance between two points
Junior Q4:
Convert array of indices to One-Hot encoded NumPy array
Mid Q5:
How to convert an array of arrays into a flat 1D array?
Mid Q6:
How to find all the local maxima (or peaks) in a 1D array?
Mid Q7:
What's the easiest way to implement a moving average with NumPy?
Mid Q8:
Extract all numbers between a given range from a NumPy array
Mid Q9:
How to convert a numeric array to a categorical (text) array?
Mid Q10:
Transpose matrix using einsum similar to np.transpose(arr)
einsum similar to np.transpose(arr) Mid Q11:
What is the difference between test[:,0] vs test[:,[0]]? When would you use one?
test[:,0] vs test[:,[0]]? When would you use one? Mid Q12:
How would you reverse a NumPy array?
Mid Q13:
How would you get indices of N-max values in a NumPy array?
Mid Q14:
Write the einsum equivalent of inner, outer, sum, and multiplication functions
einsum equivalent of inner, outer, sum, and multiplication functions Senior Q15:
Consider an array Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14], how to generate an array R = [[1,2,3,4], [2,3,4,5], [3,4,5,6], ..., [11,12,13,14]]?
Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14], how to generate an array R = [[1,2,3,4], [2,3,4,5], [3,4,5,6], ..., [11,12,13,14]]? Senior Q16:
Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z[0],Z[1],Z[2]) and each subsequent row is shifted by 1 (last row should be (Z[-3],Z[-2],Z[-1]))
(Z[0],Z[1],Z[2]) and each subsequent row is shifted by 1 (last row should be (Z[-3],Z[-2],Z[-1])) Senior Q17:
Sum over last two axis at once
Senior 
Prepare for Claude interview questions with answers for AI engineers covering the Anthropic API, Messages API, prompting, tool use, structured outputs, prompt caching, adaptive thinking, MCP, security, and production agents....
Prepare for prompt engineering interviews with answered LLM prompting, ChatGPT prompting, and Claude prompting interview questions covering context windows, few-shot learning, structured outputs, tool use, prompt caching, hallucination reduction, eva...
Prepare for AI developer and engineer interviews with 19 answered OpenClaw questions covering Gateway architecture, channels, agent workspaces, memory, MCP, model failover, multi-agent routing, security, sandboxing, approvals, and remote operations....
Prepare for AI agent developer interviews with 15 Model Context Protocol (MCP) questions covering tools, resources, prompts, JSON-RPC, transports, roots, sampling, security, and practical MCP server design....
Amazone runs the internet as we know it. Amazon Web Services (AWS) offers a comprehensive suite of machine learning (ML) services that cater to various needs and expertise levels. Follow along and learn the 23 most common AWS machine-learning intervi...
Azure Machine Learning (Azure ML) is a cloud-based service for creating and managing machine learning solutions. It’s designed to scale, distribute, and deploy machine learning models to the cloud. Follow along and learn the 23 most common Azure Mach...
Hadoop is an open-source big data processing framework. It leverages distributed computing to store and process large datasets in a fault-tolerant manner. According to recent reports, Apache Hadoop is one of the most sought-after big data skills with...
Apache Spark is a unified analytics engine for large-scale data processing. It is built to handle various use cases in big data analytics, including data processing, machine learning, and graph processing. Follow along and learn the 23 most common an...
Scala is a powerful language with functional programming capabilities that can be a good choice for data science, especially in big data and distributed computing scenarios. As an example, Apache Spark, a popular distributed data processing framework...
PyTorch popularity as a Deep Learning framework of choice is on the rise. As of December 2022, 62% of the academic papers were implemented in PyTorch whereas only 4% were for TensorFlow. Follow along and prepare effectively with these key 30 PyTorch ...
MLStack.Cafe is a biggest hand-picked collection of top Machine Learning, Data Science & Python Interview Questions. Land your next Six-Figure DS job offer in no time.
Coded with 🧡 using React in Australia 🇦🇺
by @aershov24, Full Stack Cafe Pty Ltd 🤙, Est. 2018
Privacy • Terms of Service • Guest Posts • Contacts • FullStack.Cafe
Great 
based on 65 Trustpilot 
Reviews
based on 65 Trustpilot Reviews