This was the online assessment coding problem.
Sort customers by bid amount in descending order, using a round-robin mechanism to fairly break ties among equal bidders. Iterate through the sorted list, allocating warehouse inventory greedily until stock runs out, then collect all customers who received zero items.
Pro tip: Explicitly clarify the warehouse's total inventory and whether each item type has separate stock before coding — Amazon interviewers reward candidates who surface ambiguities early, and this shows system-design thinking even in an algorithmic problem.
Ask about total inventory size, whether item counts per customer can exceed remaining stock (partial fulfillment), and confirm the round-robin tie-breaking rule. This surfaces hidden requirements and shows structured thinking.
Sort customers descending by bid amount. For customers with equal bids, assign them a round-robin order — this can be modeled by preserving their original insertion order within each tie group and cycling through them sequentially.
Iterate through the sorted (and tie-broken) customer list, subtracting each customer's desired item count from remaining inventory. Handle partial fulfillment if allowed, or skip customers whose full request cannot be met.
Track which customers received zero items — either because inventory was exhausted before reaching them or their request exceeded remaining stock. Return their IDs as the result.
Discuss time complexity O(n log n) for sorting and O(n) for allocation, totaling O(n log n). Mention that a max-heap (priority queue) is an alternative if customers arrive in a stream rather than as a batch.
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The base question is easy enough, but the follow-ups are where it gets real.
Start by clarifying requirements and constraints (scale, latency SLAs, discount types), then design a clean API contract before diving into the internal architecture. Structure your answer around separation of concerns — pricing service, discount engine, and caching layer — while explicitly calling out how each component enables future extensibility and handles large-scale load.
Pro tip: At Amazon, mentioning idempotency, eventual consistency trade-offs, and how your design aligns with their Leadership Principles (especially 'Think Big' and 'Scale') will resonate strongly — frame discount extensibility as a plugin/strategy pattern to show you're designing for a platform, not just a feature.
Ask about expected QPS, acceptable latency (p99), types of discounts anticipated (coupon codes, bulk, loyalty tiers), and whether prices need to be real-time or can tolerate slight staleness. Confirm whether the API is internal or customer-facing, as this affects auth and SLA requirements.
Design a RESTful or gRPC endpoint such as POST /cart/price with a request body containing product IDs, quantities, user ID, and optional promo codes, returning a structured response with line-item breakdown, applied discounts, and total. Emphasize versioning (e.g., /v1/) from day one to support future changes without breaking clients.
Decompose into a Pricing Service (fetches base prices from a Product Catalog), a Discount Engine (applies rules via a strategy pattern for extensibility), and an Aggregator that computes the final total. Use asynchronous batch fetching of product prices to minimize latency and avoid N+1 database calls.
Introduce a distributed cache (e.g., Redis) with a short TTL for product prices to reduce database load at high QPS, and use circuit breakers for downstream service calls. Design the system to degrade gracefully — for example, returning cached prices with a staleness flag if the catalog service is unavailable.
Model the Discount Engine using a strategy or rules-engine pattern (e.g., a chain of responsibility) so new discount types can be added without modifying core pricing logic. Store discount rules in a configuration store or database to allow non-engineer teams to update rules without deployments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was labeled the GenAI interview but the actual coding problem had nothing obviously AI-specific about it.
Frame this as a priority-based matching problem where specificity determines rank — a rule with fewer DEFAULT wildcards is more specific and should win over a more generic one. Walk through a concrete algorithm that scores or sorts candidate rules by specificity, then returns the best match for the given query tuple. Make sure to clarify edge cases like ties, no match, and all-DEFAULT rules upfront.
Pro tip: Amazon loves scalability discussions — proactively mention how your solution performs when the rule table has millions of rows or when queries arrive at high throughput, and suggest indexing or trie-based optimizations to show senior-level thinking.
Confirm the matching semantics: a rule matches a query if each key in the rule is either equal to the query value or is DEFAULT. Ask about tie-breaking rules, whether multiple matches are possible, and expected table size to scope the solution.
Assign a score to each rule based on the number of non-DEFAULT (concrete) values — more concrete values means higher specificity and higher priority. For example, with 3 keys, a fully concrete rule scores 3 and an all-DEFAULT rule scores 0.
Iterate through the rule table and collect all rules that match the query, meaning each key in the rule equals the corresponding query value or is DEFAULT. This produces the candidate set of valid matches.
From the candidate set, return the rule with the highest specificity score; if there is a tie, apply a secondary tie-breaking strategy such as rule insertion order or lexicographic key priority. If no candidates exist, return a 'no match' result.
Analyze the naive O(N) scan per query and propose optimizations such as pre-sorting rules by specificity descending so you can return the first match, or building a trie/decision-tree index on the key columns to reduce lookup time for large rule sets.
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Anchor your answer in concrete, real-world examples of AI tools you actively use (e.g., GitHub Copilot, ChatGPT, Amazon CodeWhisperer) and demonstrate a deliberate, iterative mindset toward prompt engineering. Show that you treat AI as a productivity multiplier while maintaining critical judgment over its outputs, which aligns with Amazon's emphasis on ownership and high standards.
Pro tip: Mentioning that you document and version your most effective prompts — or share them with your team — signals engineering maturity and a 'raise the bar' mentality that resonates strongly with Amazon's leadership principles.
Open by listing 2-3 specific AI tools you use and the distinct engineering tasks each supports, such as code generation, debugging, documentation, or code review. Being specific immediately establishes credibility and relevance.
Describe a real scenario where an AI tool meaningfully impacted your workflow — include the problem, how you used the tool, and the measurable outcome. This grounds your answer in evidence rather than generality.
Detail the deliberate techniques you use to refine prompts, such as adding context, specifying constraints, using chain-of-thought prompting, or iterating based on output quality. This demonstrates a systematic, engineering-minded approach to AI usage.
Explain how you validate AI-generated outputs — for example, running tests, code reviews, or cross-referencing documentation — to ensure correctness and avoid blindly trusting results. This shows you maintain ownership and high standards.
Briefly mention how you share learnings, effective prompts, or best practices with teammates to scale the benefit beyond yourself. This reflects Amazon's leadership principle of thinking big and developing others.
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Classic BFS problem, take the last node at each level.
Use a Breadth-First Search (BFS) level-order traversal to process the tree level by level, capturing the last node visited at each level as the rightmost visible node. This approach naturally maps to the problem since 'visible from the right' means the last node at each depth when viewed from right to left. Alternatively, a DFS approach tracking depth can also work by always visiting the right child first.
Pro tip: Mention both BFS and DFS solutions to show breadth of knowledge, then explain your trade-off reasoning — BFS is more intuitive for this problem, but DFS uses O(h) space on the call stack versus O(w) for BFS where w is the max tree width, which can matter for wide trees.
Confirm edge cases with the interviewer: what to return for a null/empty tree, and verify that 'right side view' means the last visible node at each level, not just right children. This shows thoroughness and avoids assumptions.
Propose BFS using a queue to traverse level by level, explaining that the last element dequeued at each level is the rightmost visible node. Briefly mention the DFS alternative to demonstrate awareness of multiple solutions.
Implement BFS with a queue, iterating through each level using the queue's current size as the level boundary, and appending the last node's value of each level to the result list. Keep the code clean and well-named.
Walk through a sample binary tree (e.g., [1, 2, 3, null, 5, null, 4]) step by step to verify correctness, showing the output [1, 3, 4]. This demonstrates confidence and helps catch any bugs.
State that both time and space complexity are O(n), where n is the number of nodes, since every node is visited once and the queue holds at most one full level of nodes at a time. Mention the O(h) space trade-off for the DFS approach.
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Use a hash map to track character frequencies as you process the stream, and a queue (or ordered structure) to maintain insertion order so you can efficiently find the first unique character. Once the stream ends, iterate through the queue to return the first character whose count is exactly one. This two-data-structure approach balances O(1) lookups with O(1) order tracking.
Pro tip: Proactively discuss the 'stream' constraint — unlike a static string, you may not be able to revisit earlier characters, so mention that your solution processes each character exactly once in O(1) time per character, which demonstrates awareness of real-world streaming limitations that Amazon's systems face at scale.
Ask whether the stream is finite or infinite, what character set is used (ASCII, Unicode), and what to return if no unique character exists. Confirm whether you need to return the result after the full stream is consumed or in real-time after each character.
Select a hash map to store character frequencies and a doubly linked list or queue to preserve insertion order of characters. Explain that the combination allows O(1) frequency updates and O(1) removal of characters that become non-unique.
For each incoming character, increment its count in the hash map; if it's seen for the first time, add it to the queue. If its count exceeds one, mark it as invalid (or remove it from the linked list if using that structure).
After the stream ends (or on demand), traverse the queue from the front and return the first character whose frequency in the hash map is exactly one. Handle the edge case where no such character exists by returning null or a sentinel value.
State that time complexity is O(n) overall with O(1) per character, and space complexity is O(k) where k is the size of the character set (bounded, e.g., 26 for lowercase letters or 128 for ASCII). Mention trade-offs such as using a fixed-size array instead of a hash map for a smaller character set.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to tell a specific, concrete story that highlights your empathy, collaboration, and ownership mentality. Focus on how you proactively identified the colleague's struggle and took deliberate steps to support them without undermining their autonomy. Tie the outcome back to team or business impact to demonstrate that helping others is a strategic, not just social, behavior.
Pro tip: Amazon deeply values 'Earn Trust' and 'Are Right, A Lot' — frame your story to show you listened carefully to understand the root cause of your colleague's struggle before jumping to solutions, as this signals both emotional intelligence and sound judgment.
Briefly describe the context — the project, the team, and your colleague's role. Establish why the situation mattered and what was at stake for the team or product.
Explain how you noticed your colleague was struggling — whether through direct observation, a 1:1 conversation, or a missed deadline. Highlight that you took initiative rather than waiting to be asked.
Describe how you took time to listen and diagnose the real problem — whether it was a technical blocker, unclear requirements, or personal bandwidth. This shows empathy and analytical thinking.
Detail the specific steps you took to help — pair programming, knowledge sharing, re-prioritizing your own tasks, or connecting them with the right resources. Be concrete about your personal contribution.
Quantify the result where possible — did the project ship on time, did your colleague grow in a skill, or did team morale improve? Reflect briefly on what you learned about collaboration or leadership.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.