The Coding / Technical Round
Every source agrees on one point: this round is explicitly not LeetCode. Companies say so directly in the job posting and repeat it to the recruiter script. What replaces it is closer to a simulation of the job itself — integrating with something messy, debugging something that half-works, and building something small enough to finish in an hour but real enough to argue about.
What to expect
Format varies by company, but the shape is consistent: a shared editor with the interviewer watching (sometimes pairing), or a take-home with a short debrief afterward. Either way, the problem is deliberately close to the actual job — integration, debugging, and production-quality code — not an abstract algorithm.
| Problem type | What it looks like |
|---|---|
| Messy real-world data | Parse a CSV or JSON export with missing fields, inconsistent types, and duplicate rows — the kind of export an actual client system produces, not a clean fixture. |
| A small integration or CLI tool | Wrap a flaky third-party API: add retries, timeouts, and a sane error message instead of a stack trace. |
| Rate limiting / backpressure | Build a simple rate limiter or queue that keeps a downstream system from falling over under bursty load. |
| Debugging an existing snippet | You’re handed code with a subtle bug — off-by-one, a race condition, a silently swallowed exception — and asked to find and fix it while narrating your reasoning. |
| Refactor for testability | Take a tangled function and pull it apart so it can actually be unit tested, without changing its behavior. |
| A small RAG or agent slice | At AI-native companies specifically: wire up a minimal retrieval step or a single tool call, close to what Intermediate Chapters 3–5 of this curriculum build. |
You will not be asked to invert a binary tree or optimize a dynamic-programming recurrence. If a round does turn out to be LeetCode-style, that’s a signal the posting borrowed the FDE title without the actual job behind it — worth noting as you evaluate the offer, not just the interview.
What actually separates a pass from a fail
The code quality bar is real but forgiving. What interviewers consistently flag — in both directions — has less to do with the final answer than with how you got there:
Clarifying questions before code
Candidates who ask “what happens with a null here?” or “should this be idempotent?” before typing are read as already thinking like someone who’ll own this in production.
Continuous narration
Silence for ten minutes reads as a red flag even if the final code is correct — the interviewer is grading how you think, not just what you output.
Catching your own bugs
Nobody expects zero bugs. Noticing one yourself, saying so, and fixing it reads far better than an interviewer having to point it out.
Clean and tested over “optimal”
A version with basic error handling and a couple of test cases beats a cleverer, faster version with neither. This isn’t a competitive-programming judge.
Pragmatic trade-off calls, said out loud
“I’d cache this in a real system, but I’ll skip it here for time” is a better answer than silently skipping it — or silently over-engineering it.
The most common failure mode isn’t a bad solution — it’s silence. A candidate who narrates a mediocre approach clearly will often score better than one who quietly writes a correct one. The interviewer can’t grade thinking they can’t see.
How to prepare
Two weeks is enough if you’re already comfortable writing code; the goal isn’t to learn to code, it’s to rebuild the specific muscle of narrating, scoping, and shipping small under time pressure.
- 1
Week 1 — rebuild fluency with the tools you already know
Not algorithmic puzzles — practical fluency. Read and modify unfamiliar code quickly, write basic tests, and get comfortable narrating out loud while you type. If Python is rusty, redo a couple of labs from this curriculum’s Intermediate track from scratch, without copying.
- 2
Week 2 — five coding exercises, five SQL exercises
Pick five small integration-style problems from the list above and finish each in under an hour, out loud, as if someone were watching. Pair it with five SQL exercises — joins, window functions, a query against a slightly denormalized schema — since SQL beyond the basics shows up in the vast majority of FDE postings.
- 3
Rehearse the take-home format too
Some companies replace the live round with a take-home. Practice writing a short README explaining your trade-offs — that write-up is graded as closely as the code.
Two things to have ready before you walk in
Beyond practicing problems, two concrete artifacts change how the whole interview reads — because they let the interviewer evaluate real work instead of a simulated forty-five minutes of it.
A public GitHub history, not a resume line
Have two or three repos an interviewer can actually open before the call — pinned, with a real README (what it does, how to run it, what you’d do differently with more time). This curriculum’s labs are already structured that way; pushing a couple of completed ones as your own is a legitimate starting point, not padding.
A live, deployed agentic AI demo — not something running on localhost
A URL beats a screen-share of your terminal every time. Take one of the agent builds from Intermediate Chapters 6–9 or the Advanced capstone and actually deploy it — a small FastAPI or Streamlit app on something like Render, Railway, Fly.io, or Streamlit Community Cloud is enough. Being able to say “here, try it” mid-interview is a different conversation than describing what it would do.
One story about debugging something in production
Not from this round specifically — save the fuller version for behavioral — but have the two-sentence version ready: what broke, how you found it, what you shipped to fix it. It comes up more than expected as a follow-up to the coding exercise itself.
Neither of these needs to be impressive at scale. A small, honestly-documented, actually-running thing beats an ambitious, half-finished one every time this comes up as a talking point.