Machine Learning Scientist (L5) - Content and Studio

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 design an A/B test to re-engage inactive users of a mobile fitness app. Assume that users are considered inactive if they haven't used the app for 30 days. Our goal is to increase the number of users who return and actively use the app. Consider the following when designing your A/B test: Control Group: This group receives no intervention. We'll use this as our baseline to measure against. Treatment Group A: Push Notification with Personalized Workout Recommendations: Users in this group receive a push notification that includes workout recommendations tailored to their past activity and fitness goals (e.g., Ready for your next challenge? Try this 30-minute HIIT workout based on your past activity!). Treatment Group B: In-App Discount on Premium Features: Users in this group receive a notification when they open the app offering a discounted rate on the app's premium features, such as personalized training plans or access to exclusive content (e.g., Welcome back! Enjoy 50% off a premium training plan this week only!). For this A/B test design, specify: The hypothesis for each treatment group. The target metric(s) you would track to measure success, and how you would measure them. The segmentation you might consider when analyzing results (e.g. by workout type, fitness level, demographics). The duration of the A/B test and the sample size needed for statistical significance. Explain your reasoning. The potential risks and how you would mitigate them.

Netflix

Tell me about a time you went above and beyond what was expected of you.