Machine Learning Engineer

Netflix

You are given the definition of a binary tree node: class Node: def init(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right Write a function is_bst(root) that takes the root of a binary tree as input and returns True if the tree is a valid Binary Search Tree (BST), and False otherwise. Definition of a BST: The left subtree of a node contains only nodes with keys less than the node's key. The right subtree of a node contains only nodes with keys greater than the node's key. Both the left and right subtrees must also be binary search trees. Example 1: Input: root = [2,1,3] Output: true 2 / 1 3 Example 2: Input: root = [5,1,4,null,null,3,6] Output: false 5 / 1 4 / 3 6 Explanation: The root node's value is 5 but its right child's value is 4. Constraints: The number of nodes in the tree is in the range [1, 10000]. -10000 <= Node.val <= 10000 Your solution should handle edge cases such as an empty tree or a tree with only one node.

Netflix

Let's discuss architecture and data modeling. Consider you're designing a system for a popular online bookstore. This bookstore needs to manage a vast catalog of books, track customer orders, and provide personalized recommendations. Walk me through your approach to designing the system's architecture and data model, keeping in mind scalability, performance, and maintainability. Be specific by considering these points: Microservices vs. Monolith: Would you opt for a microservices architecture or a monolithic architecture? Explain your reasoning, considering the bookstore's scale and potential future features. Data Storage: What types of databases would you use (e.g., relational, NoSQL)? Justify your choices for different parts of the system (e.g., catalog, orders, user profiles). Data Model: Describe the key entities (e.g., books, authors, customers, orders) and their relationships. Provide a simplified schema for these entities, highlighting essential attributes and data types. How would you handle relationships like a book having multiple authors or a customer placing multiple orders? Scalability: How would you design the system to handle a massive increase in traffic and data volume during peak seasons (e.g., holidays)? Consider aspects like load balancing, caching, and database sharding. Search Functionality: How would you implement the search functionality to allow users to quickly find books based on title, author, ISBN, or keywords? Consider indexing strategies and search algorithms. Recommendation Engine: Briefly outline how you would design a recommendation engine to suggest relevant books to users based on their browsing history, purchase history, and ratings. What data would you need to collect and how would you analyze it? Data Consistency: How would you ensure data consistency across different microservices and databases, especially when dealing with transactions like placing an order or updating inventory levels?

Netflix

Tell me about the most challenging product you've ever worked on. To fully explain the challenges, please address the following points: Describe the product: What was its purpose? Who was the target audience? What key features did it have? Explain the challenges: What specific technical or non-technical hurdles did you face during its development or maintenance? For example, did you struggle with scalability, performance, security, ambiguous requirements, tight deadlines, or limited resources? Be specific. Discuss your role: What were your responsibilities within the team? How did your skills and experience contribute to overcoming these challenges? Explain the solutions: What strategies or techniques did you employ to address these challenges? Did you have to learn new technologies, implement innovative solutions, or collaborate with other teams to succeed? Provide concrete examples. Describe the outcome: What was the result of your efforts? Did you successfully overcome the challenges? What impact did the product have on the users or the business? What lessons did you learn from this experience? For instance, imagine you were developing a real-time data analytics platform for a financial institution. A significant challenge was processing and analyzing massive volumes of data with low latency. You might discuss how you implemented distributed computing techniques, optimized database queries, and worked with data scientists to develop efficient algorithms. Or perhaps you were building a mobile app for a healthcare provider, and you struggled with ensuring patient data privacy and security. You could explain how you implemented end-to-end encryption, followed strict security protocols, and conducted thorough security audits. Or maybe you were tasked with modernizing a legacy system, and you encountered compatibility issues and resistance to change. You could describe how you carefully planned the migration process, communicated the benefits of the new system to stakeholders, and provided training to users.