The Complete Collection of Data Science Interviews – Part 2

The second part covers the list of Data Management, Data Engineering, Machine Learning, Deep Learning, Natural Language Processing, MLOps, Cloud Computing, and AI Manager interview questions.

The Complete Collection of Data Science Interviews – Part 2
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Editor's note: For the full scope of repositories included in this 2 part series, please see The Complete Collection of Data Science Interviews – Part 1.


The second part is on more advanced questions related to complex algorithms, processes, and tools. These interviews will prepare you for role-specific jobs. For example, MLOps engineering questions are related to machine learning algorithms, automation, pipelines, experiment monitoring, and industrial standards. 

The two-part collection of data science interview questions will help you prepare for all data-related jobs. The collection of questions are divided into categories based on subject and data field.


Table of Contents


    1. Data Management Interview Questions
    2. Data Engineering Interview Questions
    3. Machine Learning Interview Questions
    4. Deep Learning Interview Questions
    5. Natural Language Processing Interview Questions
    6. MLOps Interview Questions
    7. Cloud Computing Interview Question
    8. AI Manager Interview Questions


Data Management Interview Questions


For a data scientist, data management skill is a pulse point. You will be asked questions on data governance, software analysis and design tools, litmus test, SQL, java, and big data management tools. A Data manager’s job is to create and enforce policies for effective data management.


Sample Question: “Why is a disaster recovery plan vital for all companies using data systems?”



Data Engineering Interview Questions


Data engineering questions are all about your experience. The big companies won’t risk hiring college graduates. To clear a data engineering interview, you must know about popular data orchestration tools, SQL and no SQL databases, data warehouse, analytical engineering, batch processing, and streaming. 


Sample Question: “What are the design schemas of data modeling?”



Machine Learning Interview Questions 


I know machine learning is all over the internet, but its technical interview is hard. Most people don't even know basic terms. To make sure you pass this stage with flying colors, I want you to focus on machine learning algorithms and frameworks, managing data, handling various data types, and creating solid models that work well in production. 

Message for experienced machine learning engineers: “Don’t think you know everything. You will fail if you take it lightly.”


Sample Question: “What is Bias and Variance in a Machine Learning Model?”



Deep Learning Interview Questions


In my opinion, deep learning interviews are the hardest. I was asked to optimize a model inference on multiple Nvidia GPUs. Apart from deep learning algorithms, you must know data normalization, activation functions, dropout and batch normalization, advanced computer vision techniques, and data augmentation. 

There are so many things to cover, and you must be prepared for it. The deep learning engineers earn $160K+ a year -, and only top professionals are selected for the roles. 


Sample Question: “What do you understand by transfer learning? Name a few commonly used transfer learning models.”



Natural Language Processing (NLP) Interview Questions


Before you appear for an interview, make sure you have experience in processing text, audio, and image datasets. Furthermore, you will be asked about Bag of Words, TF-IDF, Named Entity Recognition, Regular Expressions, advanced NLP Python libraries, transformers architects, deep learning frameworks, and large language models. 

Due to Huggingface, most companies will ask you about training, validation, and deploying large language model solutions on the cloud. 


Sample Question: “What is Parsing in the context of NLP?”



MLOps Interview Questions


If you don’t have experience in MLOps, you won’t get far in the interview stage. It means you have experience in training, validating, and deploying models. The MLOps questions are related to machine learning life cycle, experiment tracking, orchestration and ML pipelines, model deployment, model monitoring in production, and understanding of best practices from software development.  


Sample Question: “Explain about data and concept drift”



Cloud Computing Interview Question 


Handling the cloud instance is becoming necessary for data scientists. Experienced cloud engineers can save costs and provide the best storage and computing solutions. You must be able to answer questions related to one of the prominent cloud service providers such as AWS, Azure, and Google. The questions revolve around scalability, database management, handling APIs, cost-saving solutions, and deploying models. 


Sample Question: “What is the difference between scalability and elasticity?”



AI Manager Interview Questions


AI managers are experienced data scientists or product managers. To get this job, you need to showcase management and data science skills. The interview question mainly revolves around data acquisition, solving business problems, understanding the data, managing the data team, machine learning life cycle, and metric and performance monitoring. 


Sample Question: “What technical metrics do you use for measuring classification model performance?”



Final Thoughts


Specialized jobs in the data field are high in demand. If you possess the experience, you can smoothly get into the top companies. To help you clear the early stages of technical interviews, I have curated extensive lists of top data science interview questions. 

So what are you waiting for?

The previous part consists of:

    1. Behavioral Interview Questions
    2. Situational Interview Questions
    3. Statistics Interview Questions
    4. Python Interview Questions
    5. R Interview Questions
    6. SQL Interview Questions
    7. Data Analytics Interview Questions
    8. Business Intelligence Interview Questions

This is the 4th edition to data science collection series, check out:

    1. The Complete Collection of Data Science Cheat Sheets – Part 1 and Part 2
    2. The Complete Collection of Data Repositories – Part 1 and Part 2
    3. The Complete Collection of Data Science Books – Part 1 and Part 2

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.