At 3 PM in the Zoom window, the Apple recruiting team’s interviewer offered a gentle smile: “Hello, let’s get started.” My nerves kicked in, but my small iPad was cleverly tucked out of view, receiving real-time prompts from CSOahelp that kept my thoughts organized.

The problem flashed on my screen as follows:
Problem (Original):
Imagine you are building a proxy (edge layer application) API endpoint which receives a JSON payload. But, you don’t know the structure of this payload. Write a function which extracts values from all JSON fields which has a type of "string" and returns them as an array of strings.

Translated:
Suppose you are constructing a proxy (edge-layer) API endpoint that receives a JSON payload of unknown structure. Write a function that traverses all fields, collects values of type string, and returns them in an array.

At that moment, my iPad popped up several clarifying questions supplied by CSOahelp Interview Assistance. As practiced, I asked these one by one to demonstrate my thought process and buy time for the instructor to draft code.

To stay in sync with the interviewer, I quickly asked:

• Will arrays and nested objects appear in the JSON?
• Are only standard serializable types included?
• Should the returned array allow duplicates?

The interviewer nodded to each, giving me a sigh of relief. With these details confirmed, we gained a few extra minutes—just enough for CSOahelp’s assistant to produce the next code snippet.

The assistant promptly wrote a Python solution to minimize code and ease transcription. With the interviewer’s approval, I then transferred the pseudocode from my iPad to the interview platform. It looked like this:

        
def extract_strings(payload):
    results = []

    def dfs(node):
        if isinstance(node, str):
            results.append(node)
        elif isinstance(node, dict):
            for value in node.values():
                dfs(value)
        elif isinstance(node, list):
            for item in node:
                dfs(item)
        # ignore other types

    dfs(payload)
    return results

# Example usage
sample = {
    "user": {"name": "Alice", "roles": ["admin", "editor"]},
    "metadata": {"version": 1, "tags": ["python", "json"]},
    "notes": "Proxy layer initialized"
}
strings = extract_strings(sample)
# ['Alice', 'admin', 'editor', 'python', 'json', 'Proxy layer initialized']
        
    

Thanks to the instructors’ tip to maintain eye contact, I wasn’t worried the interviewer would spot my glances at the iPad. I transcribed confidently and quickly.

The interviewer looked surprised at my speed—but the interview wasn’t over yet.

CSOahelp then pushed another tip:

“Consider performance. If node count is in the millions, use concurrent.futures.ThreadPoolExecutor to process partitions concurrently; add optional timeout controls.”

A few lines of fine print flickered at the bottom:

Concurrent DFS → ThreadPoolExecutor.map + cache hot paths + timeout fallback

“In production, to prevent blocking on deep or massive structures, we can split DFS into tasks, use ThreadPoolExecutor or asyncio for parallel traversal, limit concurrency with semaphores, and prefetch hot paths with a cache,” I added smoothly.

The interviewer smiled: “Very thorough—you considered scalability and reliability.”

When the call ended, I exhaled deeply as my iPad fell silent. With CSOahelp’s support, I not only organized my thoughts swiftly but also received key performance and engineering-practice reminders. A few hours later, the email arrived:

We’re impressed by your clarity and depth. Welcome aboard!

I’m so excited—I never imagined that someone like me, who’s too lazy to grind problems, could land a role at a big tech company~

If you’re preparing for algorithm and system design interviews at Apple, Meta, TikTok, etc., but aren’t sure how to break down problems and handle edge cases, get North American interview secrets. We also offer interview proxy, interview assistance, OA writing, and more to help you land your dream job sooner~

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