--- title: Data Science Interview Questions: The Complete Prep... description: A 45-question prep guide for data science and ML engineer interviews, organized by topic: Python, statistics, machine learning, deep learning,... canonical: https://daily.dev/posts/data-science-interview-questions-the-complete-prep-guide-2026--ezb4atsib twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Data Science Interview Questions: The Complete Prep Guide (2026) | daily.dev og:description: A 45-question prep guide for data science and ML engineer interviews, organized by topic: Python, statistics, machine learning, deep learning,... og:url: https://daily.dev/posts/data-science-interview-questions-the-complete-prep-guide-2026--ezb4atsib og:image: https://api.daily.dev/og/posts/ezb4aTsIB.png og:image:alt: Data Science Interview Questions: The Complete Prep Guide (2026) og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Data Science Interview Questions: The Complete Prep Guide (2026) **[Medium](https://daily.dev/sources/medium_js)** · 15 min read · 0 upvotes · 0 comments ## Summary A 45-question prep guide for data science and ML engineer interviews, organized by topic: Python, statistics, machine learning, deep learning, MLOps/deployment, SQL, and generative AI/LLMs. Includes case-study style questions, tips for freshers vs. experienced candidates, a comparison of Data Scientist vs. ML Engineer interview focus, common candidate mistakes, and a seven-day study plan. Links to a paid third-party course for deeper practice. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between L1 and L2 regularization in machine learning? L1 regularization (Lasso) adds the absolute value of coefficients to the loss function, which can drive some coefficients to zero and effectively perform feature selection. L2 regularization (Ridge) adds the squared value of coefficients, shrinking them but rarely to exactly zero. L2 is generally considered more stable for correlated features. _Comparing regularization techniques before a model review is easier with data science reading curated on daily.dev._ ### What is the difference between a model registry and a feature store in MLOps? A model registry tracks model versions, metadata, and lineage across the machine learning lifecycle, while a feature store centralizes and serves features consistently for both training and inference. Both are considered core MLOps infrastructure components, with the registry focused on model artifacts and the feature store focused on data consistency. _Engineers piecing together MLOps infrastructure can follow deployment and tooling coverage on daily.dev._ ### What is the difference between a Data Scientist interview and an ML Engineer interview? A Data Scientist interview emphasizes statistics, experiment design, modeling, and case-study reasoning, while an ML Engineer interview emphasizes production coding, deployment, CI/CD, containerization, and system design. Both share a common core of Python, machine learning fundamentals, and SQL, so candidates targeting either role should prepare that shared base first. _Deciding how to split prep time between data science and ML engineering roles gets easier with guidance on daily.dev._ ## Similar posts on daily.dev - [Data Analytics Interview Questions: The Complete Prep Guide \(2026\)](https://daily.dev/posts/data-analytics-interview-questions-the-complete-prep-guide-2026--21igcaext) · Medium · 0 upvotes · 0 comments - [How to Crack Machine Learning System-Design Interviews](https://daily.dev/posts/how-to-crack-machine-learning-system-design-interviews-bmomunu3w) · Towards Data Science · 1 upvotes · 0 comments - [AI Basics: Five Interview Questions, Answered](https://daily.dev/posts/ai-basics-five-interview-questions-answered-dxw5qjwvz) · SQL Authority · 1 upvotes · 0 comments - [What AI Engineer Interviews Really Test: A Practical Guide to Preparing for Modern AI Roles](https://daily.dev/posts/what-ai-engineer-interviews-really-test-a-practical-guide-to-preparing-for-modern-ai-roles-r5libav3b) · SitePoint · 1 upvotes · 0 comments - [Uncovering Data Science: Skills, Careers and Education](https://daily.dev/posts/uncovering-data-science-skills-careers-and-education-6ovxfqh2y) · databricks · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#data-science](https://daily.dev/tags/data-science), [#sql](https://daily.dev/tags/sql), [#interview-questions](https://daily.dev/tags/interview-questions) [View this post on daily.dev](https://daily.dev/posts/data-science-interview-questions-the-complete-prep-guide-2026--ezb4atsib) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Data Science Interview Questions: The Complete Prep Guide (2026)","url":"https://daily.dev/posts/data-science-interview-questions-the-complete-prep-guide-2026--ezb4atsib","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/data-science-interview-questions-the-complete-prep-guide-2026--ezb4atsib"},"datePublished":"2026-08-24T06:05:01.658Z","dateModified":"2026-09-13T19:08:29.250Z","description":"A 45-question prep guide for data science and ML engineer interviews, organized by topic: Python, statistics, machine learning, deep learning,...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/d6ab0a9a223ee01bc82d647f54feb2d2?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/d6ab0a9a223ee01bc82d647f54feb2d2?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/data-science-interview-questions-the-complete-prep-guide-2026--ezb4atsib","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,python,data-science,sql,interview-questions","timeRequired":"PT15M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Data Science Interview Questions: The Complete Prep Guide (2026)"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/data-science-interview-questions-the-complete-prep-guide-2026--ezb4atsib#faq","mainEntity":[{"@type":"Question","name":"What is the difference between L1 and L2 regularization in machine learning?","acceptedAnswer":{"@type":"Answer","text":"L1 regularization (Lasso) adds the absolute value of coefficients to the loss function, which can drive some coefficients to zero and effectively perform feature selection. 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