Adobe AI Interview Questions — Acing the AI Interview

There are up to 400,000 installs per day of Acrobat Reader, just from the three major app stores.

Annually Adobe Experience cloud processes 155 trillion transactions on average. The amount of data those transactions generate alone should be a very enticing proposition for a Data Scientist. Adobe is a household name in the creative business but it is also well known across all industry verticals as the inventor of the PDF file format.

At Acing AI, the aim is to help you to get into Data Science and AI. I have profiled some of the best technology companies and written articles about AI interviews at Microsoft, Google, Amazon, Netflix, LinkedIn, Ebay, Twitter, Walmart, Apple, Facebook, Zillow, Salesforce, Uber, Intel, Tesla and most recently IBM. This has led to being the top writer in Artificial Intelligence on Medium. The AI interview preparation guides Part 1, Part 2 go over the details which help you ace any AI interview. Acing AI Portfolios helps you to showcase your AI work. Expert interviews and analyses gives you a sneak peak into the lives of AI/Data Science Leaders and analyses of AI tech companies. Now onto the Adobe interview questions article….

Photo by Lost Co on Unsplash

At Adobe Data Scientists work on the digital marketing stack. They help target better customers as well as research to improve back end to serve their customers better. Adobe Careers also have a short video explaining about the work their Data Scientists do. Adobe also has a product called Adobe analytics. Data Scientists to help build the product. Hence, Adobe has a unique position where Data Scientists can be internally improving the ROI for customers or maybe helping to build Data related products. Very few companies have both these kind of opportunities.

Interview Process

The Adobe Data Scientist interview process is a rigorous one. There are two phone screens, one for coding and another one for statistics. Once you get past that, they call you onsite. The onsite consists of 4–5 interviews with the entire hiring team. Depending on the team, usually you are also expected to do a short presentation on your PhD thesis.

Important Reading
Source: Dataconomy — Putting the P in API
AI/Data Science Related Questions
  • How would you design a recommendation system?
  • Swap two integers without using a temporary variable.
  • How to calculate the most frequently occurring entry on a large data set over different servers?
  • From a given order table, describe the different data analysis that can be done.
  • Write term frequency–inverse document frequency function.
  • What is over fitting?
  • What is the degree of freedom for lasso?
  • Given two sorted arrays of integers, write a program to find a number from each array such that their sum are closest to some integer K.
  • Check if an integer is a palindrome without converting the integer to a string.
  • Generate all valid parentheses pairs from a string which contains parenthesis.
  • Create confidence intervals for a large finite sampled population
  • Describe Logit and Probit models and explain the difference between them.
  • What is a correlation colored plot?
  • Describe your any one of your research topics in detail and explain the difference between your methods and classic methods
  • Explain the difference between Logistic Regression and Collaborative Filtering.
Reflecting on the Questions

Adobe has been hiring Data Scientists for a long time. Their questions reflect the experience required in your own topic (thesis presentation) coupled with some coding questions across the different interviews they conduct. They are looking for well rounded data scientists who can write code as well along with being deep into statistics. They questions are also based enhancements to their existing products. Creative learning and deep statistical knowledge can surely get you into the company loved by most creative professionals.

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The sole motivation of this blog article is to learn about Adobe AI and its ML technologies helping people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.