"Implement a program that finds the shortest path of links between one Wikipedia URL and another."
This was a real question from an Apple technical interview — and a perfect example of how CSOAHELP’s remote interview support can make the difference between freezing up and confidently moving forward.
One of our clients was presented with this challenge during their Apple interview: given two Wikipedia page names, build a program that finds the shortest path of links from one to the other. Think of it as building your own version of "Six Degrees of Wikipedia." The problem had two parts: Part 1 focused on paths with a depth of less than 3, while Part 2 extended the crawl depth to 3. While it may look like a textbook BFS problem, it was packed with hidden complexity:
- How do you efficiently fetch Wikipedia page links?
- How do you control crawler depth to avoid exponential blowup?
- How do you design and communicate an architecture under time pressure?
For someone without prior exposure to this type of problem, it’s easy to get stuck. That’s where our real-time support stepped in and turned the tide.
On the day of the interview, we activated our agreed-upon support setup — a secondary device connected and tested in advance. As the interviewer delivered the question over Zoom and explained the test constraints (start with crawl depth under 3), the candidate began organizing their thoughts but was clearly nervous. Immediately, we provided this text prompt through the silent secondary device:
"Think of Wikipedia pages as graph nodes and hyperlinks as edges. Use BFS to find the shortest path."
This instantly helped the candidate reframe the problem into a familiar graph traversal model. Then we followed up with another core insight:
"Use a queue for BFS and a Set to track visited pages. For each level, fetch all hyperlinks (child nodes) and check for the target."
The candidate relayed this line of thinking to the interviewer and began sketching out pseudocode. Soon after, they needed to explain how they would fetch links. Although a real web crawler wasn't required, the candidate had to show they understood the design of such a function.
We quickly prompted:
"Wrap link-fetching logic in a method like fetchLinks(String pageTitle); simulate with static data or outline use of Wikipedia's API."
The interviewer approved of this modular, scalable design. Next came Part 2: handle crawl depth up to 3. This increased the search space significantly and demanded a strategy for limiting scope and reconstructing the path.
Sensing hesitation, we stepped in again:
"Add a depth counter in BFS — increment per level, skip further expansion once depth exceeds 3. Track parents in a map to rebuild the final path once target is found."
Armed with this, the candidate confidently walked through:
- Enqueuing the source page with depth = 0
- Fetching all links at each BFS level
- Ending early if the target was found
- Using a parent map to reconstruct the link path
When the coding portion arrived, we shared reusable code snippets to support the candidate’s implementation: path reconstruction logic, queue setup, and visited set usage. The candidate verbally paraphrased and transcribed them smoothly. Both Part 1 and Part 2 test cases passed.
The interviewer was impressed and remarked, “Your structure is clean, your code isn’t overcomplicated, and your logic is well-thought-out.” In truth, that clarity was the result of precise support running in the background.
Later in the interview, the interviewer asked, "What challenges would arise if this were scaled to millions of Wikipedia pages?" Thanks to another prompt from us, the candidate replied:
"The main issues would be search space explosion and network latency. I’d implement caching, throttle fetch rates, set concurrency limits, deploy distributed crawlers, and use heuristics like click popularity to prioritize link paths."
This answer highlighted both technical breadth and an applied product mindset.
Truthfully, this wasn’t the hardest algorithmic challenge — but the real difficulty came from:
- Staying clear-headed under time pressure
- Communicating ideas precisely
- Making the right architectural calls on the fly
That’s exactly where CSOAHELP’s remote support makes a critical difference.
We operate in silent observation via a secondary device. Candidates receive well-timed textual prompts — no interruptions, no audio — just clear guidance and structured thinking when it matters most. You speak and type your own words. We just make sure they’re your best ones.
This case perfectly reflects what our service is designed to do. Apple’s interview process no longer revolves around trick questions. Today’s emphasis is on structured thinking, practical modeling, and strong communication. Most candidates don’t fail because they don’t know the answer — they fail because they can't explain it well or lose their footing under stress.
This success story wasn’t a fluke. We’ve supported hundreds of candidates who’ve landed offers from Apple, Meta, Stripe, Google, Amazon, and more — through technical, system design, and behavioral interviews.
If your interview is coming up, and you’re worried about freezing, fumbling, or drifting off-topic, let CSOAHELP be your secret weapon. You can walk in clear, calm, and focused — just like this candidate did.
You’re not underqualified — you just haven’t had the right support.
We bring the method. You bring the talent.
CSOAHELP — we don’t interview for you, but we help you win the interview.
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.
