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      Entretiens chez Crossover for WorkEntretiens d’embauche pour Senior Vice President of Technical Product Management (Advertised At $400K Per Annum) chez Crossover for WorkEntretien chez Crossover for Work


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      Entretien pour Senior Vice President of Technical Product Management (Advertised At $400K Per Annum)

      23 sept. 2019
      Candidat à l'entretien anonyme
      Madrid
      Aucune offre
      Expérience négative
      Entretien moyen

      Candidature

      J'ai postulé en ligne. Le processus a pris 2 jours. J'ai passé un entretien chez Crossover for Work (Madrid) en sept. 2019

      Entretien

      At no point in the process did I ever have contact with a human being. Its 100% automated. Their process is entirely automated and comprises of the following: - Basic Fit Questions (5 mins) - Cognitive Aptitude Multi-choice (15 mins) - English-proficiency test - voice recording (10 minutes) - Multiple-choice Test (20 minutes) - Free Response Exercises - effectively design solutions for them (3 hours) For the free response part all of your answers have to be put into separate Google Documents and shared with them. Surprisingly, Google's activity log shows no activity of anyone ever having looked at it. Yet someone apparently reviewed them and failed me? I later reached out asking for feedback, but was told I was to consider where I went wrong, and to try again in 6 months.

      Questions d'entretien [1]

      Question 1

      "You are asked to design an application to process mobile phone usage data on behalf of a large global telecom provider. The telecom provider wishes to know their top subscribers by revenue based on each subscriber's data usage. The raw usage data resides in thousands of log files produced daily. Each log file can be up to 256MB in size and they all contain 1-line data records, each representing 1-minute of data usage for a single subscriber. Each record includes 10 numeric fields that measure the data usage across various usage categories. The record also contains information such as the subscriber ID, and timestamp. The files are compressed and stored in an S3 bucket organized by year, month and day. The revenue calculation requires a number of computations to combine and price each of the 10 different usage categories, but each data record can be priced independently. You are going to solve this problem using the Map/Reduce pattern. Submit a copy of this M/R spec template: http://bit.ly/2W2TUVA describing how to implement the various phases of Map/Reduce."
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