The moment the interviewer finished explaining the question, our candidate was already a bit lost. He didn’t say anything, but we could tell. On the other side of the Zoom screen sat the DoorDash engineer, sharing a problem description that looked like a shortest-path scenario. The challenge wasn’t just algorithmic—it was the overload of details, time pressure, and confusion.

That’s exactly when CSOAHELP stepped in to make a difference.

We already knew this role was for a systems engineer in delivery logistics, involving map traversal, route planning, and warehouse algorithms. We had prepped our client with similar problem patterns. This specific question was expected in form, but tricky in implementation.

Here was the original interview question:

"You are given a city represented by a grid, with 'X' as blocked roads, spaces as open roads, and 'D' as DashMarts. You are also given a list of locations. For each location, return the shortest distance to the nearest DashMart using open roads. If it's unreachable, return -1."

At first glance, it’s a typical breadth-first search question, but the traps are subtle.

The city is represented as a character matrix, not a standard graph, so the approach needs to adapt. There are multiple query locations, each requiring a distance lookup—so performance matters. Some inputs may be invalid, like [200,200], and must be filtered or gracefully handled. Most importantly, there are multiple DashMarts. Instead of running a search from each query point, the efficient way is to reverse the logic: start from all DashMarts at once with a multi-source BFS, then cache distances.

This is when our real-time assist tool activated. Initially, the candidate started from each location, trying to find the closest DashMart—but quickly got stuck. He paused, glanced at the helper screen, and saw our first suggestion:

"Start BFS from all DashMarts simultaneously. Build a table of minimum distances to all accessible points. Then, each location query becomes a direct lookup."

This flipped his thinking immediately. He rewrote the traversal logic to begin from DashMarts, recording the shortest path to each point. In front of the interviewer, he explained, "I chose to do BFS from DashMarts because there are fewer of them than query locations, and it's more efficient to process that way."

The interviewer nodded. First round—stabilized.

Then the follow-up questions came, raising the bar. The interviewer asked, "What if the city map is dynamic—some roads become blocked or open over time? Would your solution still work?"

We instantly sent our second prompt:

"Treat the map as a mutable graph. Snapshot the current state before each search. If updates are frequent, introduce incremental updates or a weighted A* search with lazy updates to reduce recomputation."

The candidate repeated naturally, "If the map changes dynamically, I’d snapshot the current state for each query. With frequent updates, I’d move to A* search with heuristic estimates and lazy updates for efficiency."

The interviewer continued, "What about scalability? What if you had to support thousands of simultaneous queries?"

We provided the next message:

"Precompute and cache all DashMart-to-point distances in a high-performance key-value store like Redis. Querying a location becomes a cache lookup. If missing, trigger an async BFS to compute and store the result."

The candidate responded, "I’d precompute the DashMart distances and store them in Redis. Each location query would hit the cache directly. If there’s a miss, I’d spin up an async task to fill in the data."

He never froze. While his actual technical skill wasn’t enough to handle this problem alone, our real-time text prompts walked him through the reasoning, response structure, and even code frameworks he could repeat confidently.

This is the real value of CSOAHELP remote interview assistance.

We don’t answer for you. We help you build the language, technical framing, and delivery to sound like someone who truly knows what they’re doing. We even predict what the interviewer is likely to ask next and help you steer around traps.

This candidate passed the DoorDash technical interview and advanced to the system design round. He told us afterward, "If it weren’t for you guys, I would’ve choked on that first question."

This is just one of many stories we see every day.

We’ve helped candidates with weak technical backgrounds, poor expression skills, or limited English land jobs at Google, Stripe, Amazon, Apple, and other top-tier companies.

What you see is their polished performance in interviews.

What you don’t see is our silent prompts, flowing constantly from the second screen.

CSOAHELP—remote interview assistance that doesn’t speak for you, but makes sure every word you speak sounds sharp, confident, and professional.

If you’re getting ready for a Zoom interview, let us be your invisible teleprompter—so all you have to do is show up and speak your best.

经过csoahelp的面试辅助，候选人获取了良好的面试表现。如果您需要面试辅助或面试代面服务，帮助您进入梦想中的大厂，请随时联系我。

If you need more interview support or interview proxy practice, feel free to contact us. We offer comprehensive interview support services to help you successfully land a job at your dream company.