Amazon interviews are known for pushing candidates to their cognitive and emotional limits. You're expected to think fast, structure cleanly, and communicate clearly—all under time pressure and with a senior engineer silently watching your every move.

The candidate featured in this interview was no exception. But unlike most, they weren’t facing it alone.

With CSOAHELP’s real-time remote interview assistance, we supported them across every step—understanding ambiguous questions, structuring thoughts, debugging on the fly, and staying calm under fire.

Here’s how we helped them tackle three tough questions and walk away with a next-round invitation.

🔹 Question 1: Most Frequently Used Item Cache

Design a data structure that evicts the most frequently used item
Implement the cache class:
* Cache(int capacity) Initialize the Cache with positive size capacity
* int get(int key) Return the value of the key if the key exists, otherwise return -1.
* void put(int key, int value) Update the value of the key if the key exists. If the capacity is reached, remove the most frequently item and add the new item

This isn’t your standard LRU cache—it’s a twist on LFU (Least Frequently Used). The implementation is tricky: it requires you to track frequency counts, efficiently update them, and ensure get and put both run in near-constant time.

💡 How CSOAHELP helped:
We guided the candidate to identify the correct data structure strategy: a combination of Map<Key, Value>, Map<Key, Frequency>, and a reverse map of Map<Frequency, LinkedHashSet<Key>>. Then we helped them design the promotion logic on get() and insertion logic in put(), ensuring the most frequent item gets evicted on overflow. We even suggested a sketch of how to do frequency bucket management to avoid performance traps.

✅ Final result: Candidate implemented a working solution and handled follow-ups smoothly.

🔹 Question 2: Garbage Baskets Problem (Packing Variation)

there is a new city being built, city is trying to find
how many baskets they need of size (k), to contain
all the garbage (list of items)
generated in the city

eg:
input garbage sizes {6, 3, 5, 4, 2, 1, 9}
k = 10

This is essentially a bin-packing approximation. Most candidates freeze because the question doesn’t specify: is this best-fit, first-fit, or optimized solution? What’s acceptable time complexity? Is reordering allowed?

💡 How CSOAHELP helped:
We immediately clarified the intent and advised treating it as a greedy packing problem. Our mentor recommended sorting the garbage sizes in descending order, then pairing largest with smallest that fits. We also reminded the candidate to handle edge cases (like a single garbage item bigger than k), and walked through a dry run live to validate their logic.

✅ Final result: Candidate wrote a clear and working greedy algorithm. Interviewer was impressed with the structured reasoning.

🔹 Question 3: Lowest Common Ancestor in Binary Tree with Parent Pointers

data class Node<T> (
    val data: T,
    val left: Node<T>,
    val right: Node<T>,
    val parent: Node<T>
)

/*
 *
 *          R
 *         /
 *        C
 *       / \
 *      D   B
 *     /
 *    A
 *
 * Given a tree like the one above, lca(A, B) should return C
 */
fun lca(a: Node<T>, b: Node<T>): Node<T> {
    //
}

This is a classic—but only if you’ve practiced the parent-pointer variant. Otherwise, it’s easy to get lost traversing upward without a proper visited-set or depth equalizer.

💡 How CSOAHELP helped:
We advised a simple solution using a Set<Node> to store the path from a to the root, then walked b up until it hit something in the set—first match is the LCA. Clean, intuitive, and works even without subtree knowledge. Our mentor also reviewed the Kotlin syntax and helped adjust edge case handling (like null parents).

✅ Final result: Candidate wrote correct code and clearly explained time/space complexity.

Why CSOAHELP Makes the Difference

In this Amazon technical interview, the candidate succeeded because they weren’t left alone to “figure it out” under pressure. Here’s what they had on their side:

✅ Real-time screen sharing and silent observation
✅ Live guidance on structuring and strategy
✅ Code-level nudges and clarification when stuck
✅ Post-interview feedback and improvement notes

🧠 Don’t Let Nerves Sink Your Interview. Outsmart the Pressure.

CSOAHELP is built for candidates who already know how to think, but just need someone in their corner when the stakes are high.

Whether it’s real-time remote assistance, ghost mode coaching, or even full proxy sessions—we help you land the offer.

📬 Learn more or book a session:

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~

Contact Us:
Email: ceo@csoahelp.com