Software Development Engineer, Conversational AI Modeling and Learning

Amazon

Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”. Details: Your solution should be runnable and demonstrate the output. Focus on clean, readable code. Consider edge cases and error handling (though not strictly required for this basic version). Provide a brief explanation of your approach and any assumptions you made. Example: 1 2 Fizz 4 Buzz Fizz 7 8 Fizz Buzz 11 Fizz 13 14 FizzBuzz ... Variations (optional, for a more challenging exercise): Allow the user to input the upper limit of the range (instead of always going to 100). Allow the user to input the numbers to use for Fizz and Buzz (instead of always using 3 and 5). Allow the user to input the words to use for Fizz and Buzz (instead of always using Fizz and Buzz). Handle invalid inputs gracefully. This question assesses a candidate's basic programming skills, understanding of loops and conditional statements, and ability to write clean, understandable code. The variations allow for exploring error handling, input validation, and more flexible program design.

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

Describe a time when a plan did not go as expected.