# AI interview practice for computer science majors

Computer science majors most often start as new grad software engineers, and the interview loop for those roles almost always includes a live coding or debugging round alongside a behavioral round that many CS candidates underprepare for. Intervieux runs technical AI interviews with real code execution plus a separate behavioral track for the full loop.

The technical bar for CS interviews is well documented, algorithm questions, data structures, sometimes a take-home project. What's less talked about is that the behavioral round eliminates plenty of technically strong candidates who never practiced explaining their reasoning or handling a past-conflict question out loud.

## Where a computer science degree leads

New grad software engineer roles at a product company, a startup, or a larger tech employer are the dominant first path, sitting in the software engineering family, writing and shipping production code under a mentor or team lead. A second common path leans into data engineering or ML-adjacent roles, sitting closer to the data and analytics family, building pipelines or supporting machine learning systems rather than user-facing product code. A smaller group goes into infrastructure, security, or DevOps-leaning roles that still sit under software engineering but test a different mix of skills. All three share the same technical-plus-behavioral interview structure, with the technical depth shifting toward the specific track.

## What a computer science interview actually looks like

Expect a live coding round where you solve a problem in real time, often with the interviewer watching your process, asking clarifying questions, and pushing on edge cases once you have a working solution. Debugging exercises show up too, handed broken code and asked to find and fix the issue, which tests a different skill than writing from scratch. For data-leaning or ML-adjacent roles, expect SQL and data manipulation questions alongside general algorithm questions. The behavioral round is where a lot of strong technical candidates lose ground: expect direct questions about a project that went wrong, a disagreement with a teammate, or how you handle ambiguous requirements, and a rehearsed but generic answer stands out immediately against a specific, concrete one. Take-home projects are common for some employers instead of, or in addition to, a live round, and interviewers follow up with detailed questions about your specific implementation choices.

## How to practice for it

Run a technical AI interview with the coding and debugging challenge formats to rehearse solving and explaining a problem under real conditions, including real code execution across common languages, not just talking through pseudocode. Follow it with a comprehensive AI interview specifically for the behavioral round, since that's the piece most CS candidates skip in their prep and the piece that quietly costs offers. If you're applying to a specific posting, whether that's a product SWE role or a more data-leaning role, a job-description-based interview builds practice around that team's actual stack and focus. Practice narrating your thought process out loud while you solve a problem, not just after, since interviewers are grading how you get to the answer as much as the answer itself.

## Frequently asked questions

### How much should I prepare for algorithms versus behavioral questions?

Both, seriously. Algorithm prep gets most of the attention in CS circles, but behavioral rounds eliminate technically strong candidates constantly, so don't treat it as an afterthought you'll wing.

### Do new grad interviews still ask classic whiteboard-style algorithm questions?

Many do, though increasingly in a shared coding environment rather than a literal whiteboard. Expect data structure and algorithm reasoning either way, plus follow-up questions on time and space complexity.

### What if I bomb the coding portion but do well on the behavioral round?

It can still hurt you badly, since most new grad loops treat the technical round as a real bar to clear, not just one input among many. Prioritize technical prep if you're weaker there.

### Should I practice explaining my code out loud, or just solve it quietly?

Practice narrating your thinking out loud. Interviewers are evaluating your process and communication as much as the final working solution, and a silent solve, even a correct one, reads worse than a talked-through one.

## Related pages

- [Software engineering interview hub](/interviews/software-engineering)
- [Technical interview practice](/features/technical-interviews)
- [Technical challenges](/features/technical-challenges)
- [Browse software engineering jobs](/jobs)
- [Intervieux home](/)

## Practice before your next software engineering interview

Run a free AI interview with real code execution across coding, debugging, and SQL challenges, get scored feedback across all 5 dimensions, and apply to real SWE openings on the job board.

Start practicing free: https://www.intervieux.ai/register · Hire with Intervieux: https://www.intervieux.ai/employers/signup