2025 Graduate Software Dev Engineer

Amazon

Given a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree. According to the definition of LCA on Wikipedia: “The lowest common ancestor is defined between two nodes p and q as the lowest node in T that has both p and q as descendants (where we allow a node to be a descendant of itself). For example: If the tree is [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 1, then the expected output is 3, because the LCA of nodes 5 and 1 is 3. If the tree is [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 4, then the expected output is 5, because the LCA of nodes 5 and 4 is 5, since a node can be a descendant of itself according to the LCA definition. If the tree is [1,2], p = 1, q = 2, then the expected output is 1. Consider these constraints: The number of nodes in the tree is in the range [2, 10^5]. -10^9 <= Node.val <= 10^9 All Node.val are unique. p != q p and q will exist in the tree.

Amazon

Let's explore your problem-solving skills with a practical scenario. Imagine you're building a feature for an e-commerce website that recommends products to users based on their past purchases. The system currently uses a simple algorithm: it identifies the products most frequently bought by users with similar purchase histories and suggests those. However, you've noticed a few issues: Lack of Novelty: The recommendations are often too obvious or repetitive. Users who bought coffee are constantly recommended coffee, even though they might be interested in other related items like filters, mugs, or coffee makers. Cold Start Problem: New users with limited purchase history receive generic recommendations that aren't very helpful. Ignoring User Context: The system doesn't consider factors like the time of year (e.g., recommending winter coats in July) or current trends. Your task is to enhance the recommendation system to address these issues. Describe your approach, considering both algorithmic improvements and data enrichment strategies. Provide specific examples of how you would: Incorporate a measure of diversity to introduce novelty into the recommendations. Handle the cold start problem for new users. Leverage additional data sources (e.g., product descriptions, user reviews, social media trends) to improve recommendation accuracy and relevance. Explain what machine learning algorithms and evaluation metrics you would use to validate your improvements. How would you A/B test your changes to ensure they are positively impacting user engagement and sales?

Amazon

Tell me about a time you disagreed with a colleague. What was your approach to resolving the disagreement, and what was the outcome? How did you ensure that your interaction remained professional and respectful, even with differing viewpoints? For example, imagine you're working on a new feature for an e-commerce platform. You believe that a simplified checkout process will lead to higher conversion rates, while your colleague argues that adding more security steps is crucial, even if it adds friction for the user. Describe the situation, your reasoning, your colleague's reasoning, and the steps you took to reach a resolution that benefited the project and maintained a positive working relationship. What did you learn from this experience?