M365 - IT Application Dev Engineer, Kuiper Production Operations

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 yourself. To help me understand your background, please structure your response by including the following: A brief overview of your educational background: Where did you go to school, what did you study, and what were your key areas of focus or specializations? A summary of your professional experience: Walk me through your previous roles, highlighting the companies you worked for, your responsibilities, and the significant projects you contributed to. Be sure to mention any accomplishments or quantifiable results you achieved. Skills and areas of expertise: What are your strongest technical skills and areas of expertise? Provide specific examples of how you've applied these skills in previous projects or roles. For instance, if you're proficient in Python, describe a project where you used Python to solve a complex problem. Your motivations and interests: What are you passionate about in the field of technology, and what motivates you to pursue a career in this area? What specific aspects of software engineering or your chosen field do you find most exciting or rewarding? Your career goals: Where do you see yourself in the next 3-5 years? What are your short-term and long-term career aspirations, and how does this role align with your overall goals? Are you looking to specialize in a particular area, take on leadership responsibilities, or make a significant impact in a specific industry? By providing this structured overview, I hope to gain a better understanding of your background, skills, and aspirations, and how they align with the goals and requirements of this position.