> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-engineering/mlops-interview-questions.md). # MLOps Interview Questions An MLOps interview tests whether you can explain the production path out loud, not just run it. The question bank goes from written lists, to mock interviews you can watch, to tips and a list of questions an interviewer actually asked. Start with the written lists, which get longer as you go. [10 questions](https://360digitmg.com/mlops-interview-questions-answers) is 360DigiTMG's set of top MLOps questions and answers, aimed at aspiring machine learning engineers and DevOps professionals. [15 questions](https://www.analyticsvidhya.com/blog/2023/02/15-must-read-interview-questions-on-mlops-for-2023/) is the Analytics Vidhya list of MLOps questions you can expect in data science or machine learning interviews. The other [15 questions](https://hashdork.com/top-mlops-interview-questions/) is Jay's HashDork post, which covers what MLOps is, how it works, and its benefits. Reading answers is not the same as giving them, so the next two are videos. [Mock interview](https://www.youtube.com/watch?v=uZlYXHm8EPg) is Turing's MLOps mock interview with questions for senior MLOps developers. [MLOps at scale](https://www.youtube.com/watch?v=7AOgPspCOaQ) is the Open Data Science and AI Conference lightning interview "MLOps Engineering at Scale". The last two come from the other side of the table. (good) [MLOps tips](https://medium.com/nlplanet/mlops-tips-from-an-interview-study-952cc3b65801) is Fabio Chiusano's MLOps tips from an interview study. [Interview questions](https://medium.datadriveninvestor.com/data-scientist-mlops-interview-questions-list-from-interviewer-2022-2023-f680c1e90e9f) is Tapan Kumar Patro's list of the questions he actually faced in 2022–23, across DS and algorithms, Python libraries, math, SQL, data science, deployment, MLOps, and system tasks. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/ai-engineering/mlops-interview-questions.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.