Preparation material for Dropbox interviews

Overview

Dropbox-Onsite-Interviews

A guide for the Dropbox onsite interview!

The Dropbox interview question bank is very small. The bank has been in a Chinese forum for many years now, and we would like to make it accessible to everyone so that everyone will have an equal opportunity to prepare for the Dropbox onsite interview!

https://1o24bbs.com/t/topic/1381

Backup link: https://web.archive.org/web/20210224003004/https://1o24bbs.com/t/topic/1381


Behavioral Questions:

Talk about an impactful project that you led.

  • Teams that you collaborated with.
  • Convincing others to take action.
  • A tough decision that you had to make during that project.

A critical piece of feedback that you received from someone and what you did after that.

An important piece of feedback that you gave to someone else.

A conflict that you had with someone else.

How do you contribute to diversity and inclusion?


We do not ask for references and we do not check for references.


Coding and System Design Tips

As always, you must talk your way through the problem and explain your reasoning. You should ALWAYS talk about performance (system performance for system design and time/space complexity for the coding problems) and talk about testing, even if the interviewer does not prompt you to.

Coding Question List:

  1. Id Allocator - Create a class that can allocate and release ids. The image in the packet is wrong. Please see this image.

This question is EXTREMELY popular and is asked in most onsite interviews, even if you're not a recent graduate.

Solution

  1. Download File / BitTorrent - Create a class that will receive pieces of a file and tell whether the file can be assembled from the pieces.

This question is mostly for new graduates/phone screens.

  1. Game of Life - Conway's Game of Life - Problem on LeetCode

This question is EXTREMELY popular for phone screens.

Solution

  1. Hit Counter - Design a class to count the hits received by a webpage

This question is mostly on phone screens.

Solution

  1. Web Crawler - Design a web crawler, first single-threaded, then multithreaded.

This question is EXTREMELY popular for onsite interviews.

Solution

  1. Token Bucket

This question is somewhat popular for onsite interviews. It has a multi-threaded component.

Solution

  1. Search the DOM

This question is somewhat popular for roles with a large frontend component.

Question

  1. Space Panorama

Create an API to read and write files and maintain access to the least-recently written file. Then scale it up to a pool of servers.

Solution

  1. Phone Number / Dictionary - Given a phone number, consider all the words that could be made on a T9 keypad. Return all of those words that can be found in a dictionary of specific words.

This question is sometimes asked to college students and sometimes asked in phone screens. It isn't asked a lot in onsites.

Solution

  1. Sharpness Value - This question is usually phrased like "find the minimum value along all maximal paths". It's a dynamic programming question.

Occasionally asked in phone screens. Might be asked in onsites for new hires.

Solution

  1. Find Byte Pattern in a File - Determine whether a pattern of bytes occurs in a file. You need to understand the Rabin-Karp style rolling hash to do well.

Somewhat frequently asked in onsite interviews. Might be asked in phone screens.

Solution

  1. Count and Say - LeetCode. Follow up - what if it's a stream of characters?

Asked to college interns.

Solution

  1. Number of Islands / Number of Connected Components - Find the number of connected components in a grid. Leetcode

Mainly asked to college interns.

Solution

  1. Combination Sum / Bottles of Soda / Coin Change - Find all distinct combinations of soda bottles that add up to a target amount of soda. LeetCode

Mainly asked to IC1 candidates.

Solution

  1. Find Duplicate Files - Given the root of a folder tree, find all the duplicate files and return a list of the collections of duplicate files. LeetCode

Somewhat popular in phone screens. Less common in onsites.

Solution

On the Adversarial Robustness of Visual Transformer

On the Adversarial Robustness of Visual Transformer Code for our paper "On the Adversarial Robustness of Visual Transformers"

Rulin Shao 35 Dec 14, 2022
A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN

A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN Please follow Faster R-CNN and DAF to complete the environment confi

2 Jan 12, 2022
A pytorch implementation of Pytorch-Sketch-RNN

Pytorch-Sketch-RNN A pytorch implementation of https://arxiv.org/abs/1704.03477 In order to draw other things than cats, you will find more drawing da

Alexis David Jacq 172 Dec 12, 2022
PyTorch code for the ICCV'21 paper: "Always Be Dreaming: A New Approach for Class-Incremental Learning"

Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning PyTorch code for the ICCV 2021 paper: Always Be Dreaming: A New Approach f

49 Dec 21, 2022
Wider-Yolo Kütüphanesi ile Yüz Tespit Uygulamanı Yap

WIDER-YOLO : Yüz Tespit Uygulaması Yap Wider-Yolo Kütüphanesinin Kullanımı 1. Wider Face Veri Setini İndir Train Dataset Val Dataset Test Dataset Not:

Kadir Nar 6 Aug 22, 2022
GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery This is the code to the paper: Gradient-Based Learn

3 Feb 15, 2022
Active and Sample-Efficient Model Evaluation

Active Testing: Sample-Efficient Model Evaluation Hi, good to see you here! 👋 This is code for "Active Testing: Sample-Efficient Model Evaluation". P

Jannik Kossen 19 Oct 30, 2022
This is a library for training and applying sparse fine-tunings with torch and transformers.

This is a library for training and applying sparse fine-tunings with torch and transformers. Please refer to our paper Composable Sparse Fine-Tuning f

Cambridge Language Technology Lab 37 Dec 30, 2022
JudeasRx - graphical app for doing personalized causal medicine using the methods invented by Judea Pearl et al.

JudeasRX Instructions Read the references given in the Theory and Notation section below Fire up the Jupyter Notebook judeas-rx.ipynb The notebook dra

Robert R. Tucci 19 Nov 07, 2022
PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik

Youngjoon Lee 48 Dec 29, 2022
The spiritual successor to knockknock for PyTorch Lightning, get notified when your training ends

Who's there? The spiritual successor to knockknock for PyTorch Lightning, to get a notification when your training is complete or when it crashes duri

twsl 70 Oct 06, 2022
quantize aware training package for NCNN on pytorch

ncnnqat ncnnqat is a quantize aware training package for NCNN on pytorch. Table of Contents ncnnqat Table of Contents Installation Usage Code Examples

62 Nov 23, 2022
Implementation of "A MLP-like Architecture for Dense Prediction"

A MLP-like Architecture for Dense Prediction (arXiv) Updates (22/07/2021) Initial release. Model Zoo We provide CycleMLP models pretrained on ImageNet

Shoufa Chen 244 Dec 27, 2022
Complete the code of prefix-tuning in low data setting

Prefix Tuning Note: 作者在论文中提到使用真实的word去初始化prefix的操作(Initializing the prefix with activations of real words,significantly improves generation)。我在使用作者提供的

Andrew Zeng 4 Jul 11, 2022
The implementation of the CVPR2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes"

STAR-FC This code is the implementation for the CVPR 2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes" 🌟 🌟 . 🎓 Re

Shuai Shen 87 Dec 28, 2022
This repository is an unoffical PyTorch implementation of Medical segmentation in 3D and 2D.

Pytorch Medical Segmentation Read Chinese Introduction:Here! Recent Updates 2021.1.8 The train and test codes are released. 2021.2.6 A bug in dice was

EasyCV-Ellis 618 Dec 27, 2022
Official implementation of Monocular Quasi-Dense 3D Object Tracking

Monocular Quasi-Dense 3D Object Tracking Monocular Quasi-Dense 3D Object Tracking (QD-3DT) is an online framework detects and tracks objects in 3D usi

Visual Intelligence and Systems Group 441 Dec 20, 2022
How to Leverage Multimodal EHR Data for Better Medical Predictions?

How to Leverage Multimodal EHR Data for Better Medical Predictions? This repository contains the code of the paper: How to Leverage Multimodal EHR Dat

13 Dec 13, 2022
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation

Photographic Image Synthesis with Cascaded Refinement Networks-Pytorch (https://arxiv.org/abs/1707.09405) This is a Pytorch implementation of cascaded

Soumya Tripathy 63 Mar 27, 2022