# Implement an in-memory database in CodeSignal, with each round adding more functionality. Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/questions/6237/implement-inmemory-database-codesignal Source observed: 2026-11-05T00:11:21.404Z Last checked: 2026-11-05T00:11:21.404+00:00 Indexed: 2026-11-06T20:42:59.364+00:00 --- # Implement an in-memory database in CodeSignal, with each round adding more functionality We are Aced, formerly Exponent. This page is a community interview-question record for a coding and system-design question about implementing an in-memory database in CodeSignal, with additional functionality introduced in each round. ## Question details - **Reported interview count:** 2 interviews - **Status:** Coding - **Last asked:** 3 months ago - **Roles:** [AI Engineer](https://www.aced.io/questions?role=ai-engineer), [Machine Learning Engineer](https://www.aced.io/questions?role=ml-engineer) - **Companies:** [Ramp](https://www.aced.io/questions?company=ramp), [Anthropic](https://www.aced.io/questions?company=anthropic) - **Categories:** [Coding](https://www.aced.io/questions?type=coding), [System Design](https://www.aced.io/questions?type=system-design) ## Interview experiences Two interview experiences are associated with this question. ### Ramp · 3 months ago **Role:** AI Engineer > "My biggest issue was the machine learning round that I wasn't aware. I was told we would have a CodeSignal interview but it turned out to be a Google Collab interview. Had I known I was going to do some data analysis I would've brushed up on these skills." [Read the Ramp AI Engineer interview experience](https://www.aced.io/experiences/ramp-ai-engineer-interview-13abb1) ### Anthropic · a year ago **Role:** Machine Learning Engineer > "This process felt very different from most of the interviews I've done. There was no classic DS&A round at all. Instead, they tested raw coding implementation speed early on with database-style CodeSignal rounds, and then the final loop shifted hard into research thinking and a pretty fundamental understanding of LLMs. The most unusual part was the structure of the final: a 15-minute open-ended alignment brainstorm with almost no interviewer feedback, followed by a 55-minute Colab notebook exercise where I had to complete part of an LLM inference workflow. It also felt like a newer process when I did it, so I was mostly inferring what to expect from the emails rather than finding much info online." [Read the Anthropic Machine Learning Engineer interview experience](https://www.aced.io/experiences/anthropic-machine-learning-engineer-interview-8c0afd) ## Community answers No community answers have been contributed yet. Sign in or [sign up](https://www.aced.io/signup?src=nav&returnPath=/questions/6237/implement-inmemory-database-codesignal) to answer this question. ## Related courses ### [AI Engineering Interview Prep](https://www.aced.io/courses/ai-engineering) ![AI Engineering Interview Prep](https://images.ctfassets.net/x78yjrjc11pq/25mhKSvDOiCwC89lDj89Ah/9a5fbfacc8d93f05c238ab55baa4e5e5/fde.png?w=800&h=800&fm=webp&q=75) Prepare for AI engineering interviews at companies such as Google, Anthropic, Meta, and OpenAI. 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