Amazon Interview Questions

Pilla P. - "While managing a budget the most ways we get out more is time and resource"See full answer

Tej prakash V. - "DNS is a service for resolving hostname and provide fully qualified domain name of the host."See full answer

Joshua B. - "reaching my goal and going to the limits"See full answer

Onesimo F. - "N/A. I never had the same experience."See full answer

Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer

Exponent - "This is an Improve a Product question. Let's first go over the Improve a Product formula: Ask clarifying questions Identify users, behaviors, and pain points State product goal Brainstorm small improvements Brainstorm bolder improvements Measure success Summarize Now, let's begin! Ask clarifying questions Before we begin listing off recommendations, it's important you ask questions to ensure you and the interviewer are on the same page"See full answer

Chen J. - "Leetcode 347: Heap + Hashtable Follow up question: create heap with the length of K instead of N (more time complexity but less space )"See full answer

T I. - "This is due to sticky sessions. The load balancer is not correctly configured with sticky session option. It is likely the servers were storing session data on the server themselves (in-memory), and thus when user makes a request, the load balancer routes this to a different server than the one they started with, that second server may not recognise the user's session. This could prompt the user to log in again. One way to resolve this, is to use a centralised session storage, something like"See full answer

Matt M. - "Given the dataset does not contain many labels, it implies we cannot directly use supervised learning. I would ask more about the type of dataset we are given. Is it images, text, etc? This may inform the types of transformations we do the dataset. I can see two approaches to training Given the labels we do have, we can find a method to generate labels for the other unlabeled data. This likely will introduce some error since they may not be true labels, but it at least allows processing the"See full answer

Madhur K. - "I have worked with ML when I was a PM for Customer Due Diligence and it required recalibration to improve the matching logic (for screening and Proof of business matching with Google) and threshold analysis. In this scenario we had to extract past True positive, False positive decisions from operations and run threshold analysis to come up with a new threshold to match better and remove the noise i.e. False positives. Taking this new threshold for matching, the ML algorithm is receiving feedback"See full answer