In a recent Uber technical interview, our client faced a deceptively simple problem: determine the build order of package dependencies.

What appeared at first glance as a basic graph traversal question turned out to be a nuanced challenge in topological sorting — and our client, with csOAhelp's real-time support, successfully delivered a clean, robust solution that impressed the interviewer.

Here’s the full breakdown of the problem, our step-by-step assistance, and how it led to a successful outcome.

📄 Original Interview Question

“Given a package name, return the build order of its package dependencies.”
A dependency from package A to package B (A→B) means that package B must be built before A.

Example Dependency Graph:

Expected output:

css复制编辑["Interfaces", "Types", "Adapters", "Core", "Utils", "Service"]

🧠 Problem Summary

You're given:

• A target package name (e.g. "Service")
• A dictionary of dependencies where each key is a package and its value is a list of packages it depends on.

Return a build order list, such that all dependencies are built before the packages that depend on them.

💡 Correct Approach: Topological Sort (DFS-based)

This is a classic topological sorting problem, but with a twist: instead of sorting the entire graph, we must only compute the build order starting from the target package and including all its reachable dependencies.

✅ Real-time strategy we provided:

1. Graph Modeling:
   We structured the dependencies as a directed graph using an adjacency list.
2. Recursive DFS Traversal:
   For each package, visit its dependencies recursively before adding it to the build list.
3. Avoiding Repetition:
   We used a visited set to prevent duplicate processing of packages.
4. Post-order Appending:
   Since a package should be added only after its dependencies are processed, we append in post-order and reverse the result.

💻 Code Skeleton We Supplied Live

Here’s the actual DFS-based topological sort code we wrote and sent in real time during the interview:

def get_build_order(graph, target):
    visited = set()
    result = []

    def dfs(node):
        if node in visited:
            return
        visited.add(node)
        for neighbor in graph.get(node, []):
            dfs(neighbor)
        result.append(node)

    dfs(target)
    return result[::-1]

🚨 What Happened During the Interview

At first, the candidate attempted to iterate over the entire graph and track dependencies manually — which risked missing indirect dependencies or producing incorrect order.

Once we stepped in:

✅ We immediately outlined the correct topological sort strategy.
✅ We sent clean, recursive DFS code with naming optimized for verbal explanation.
✅ We anticipated and prepped follow-up answers — including time complexity (O(n)) and cycle detection.
✅ We ensured each talking point matched the interviewer's expectations — no guesswork, no blank pauses.

Our backend team wrote and sent the full solution in real time, allowing the candidate to present it smoothly and confidently.

🎯 Interviewer Feedback

“I liked that you used post-order DFS — that’s how we do it internally.”
“Very readable structure. This would work well in production.”

The candidate successfully passed to the next round.

🚀 Real-Time Support That Works

This wasn’t luck. It wasn’t “scripted prep” either.

It was real-time code writing + answer scripting + logical scaffolding — tailored to the exact pace and flow of the live interview.

What csOAhelp provides isn’t a "hint" or a cheat sheet.

It’s full collaboration during the interview:

💻 Live code drafting
📎 Full explanations for every logical step
🎙️ Ready-to-say follow-up responses
🧠 Design of clear, interviewer-facing narratives

📩 You Don’t Get a Second Chance at Your Visa

For many candidates, a single failed interview means losing their only shot before their visa expires.

That’s why our clients trust us to be their backend team — live, on-demand, ready to make sure they get through.

👉 Let us write, think, and win with you.

📩 If you have an upcoming interview and don’t want to take risks — contact us.
Whether it’s Booking, Amazon, Meta, or TikTok, we can provide real-time support during your interview to help you complete the question, explain your reasoning, and leave a solid impression.

One interview might decide whether you get to stay.
We’re here to help you write every line of code and stabilize every step of the process.