Innovation and Design Engineer, Worldwide Design Engineering

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 exemplified one of Amazon's leadership principles. For example, consider the 'Customer Obsession' principle. Can you describe a situation where you went above and beyond to meet a customer's needs, even when it was challenging or required you to think outside the box? What specific actions did you take, and what was the outcome? Alternatively, let's explore the 'Bias for Action' principle. Share an instance where you had to make a quick decision with limited information. What was the situation, what factors did you consider, and how did you mitigate the risks associated with acting swiftly? Or perhaps you can elaborate on a time you demonstrated 'Ownership.' Describe a project or task where you took full responsibility for the outcome, even when things didn't go as planned. How did you handle the setbacks, and what did you learn from the experience? Another good principle to discuss could be 'Invent and Simplify.' Tell me about a time you created an innovative solution to a complex problem, while keeping it simple and easy to use. What was the problem, what was your solution, and how did you ensure its simplicity? Finally, tell me about a time where you had to 'Learn and Be Curious'.