GCP Professional Data Engineer Guide – September 2020


I have recently recalled my first experience with GCP. It was in London, shortly before the 2012 Olympics, in an online gaming project, initially thought for AWS, that was migrated to App Engine – PAAS platform that would evolve to the current GCP.

My initial impression was good, although the platform imposed several development limitations, which would be reduced later with the release of App Engine Flexible.

Coinciding with Tensor Flow’s launch as an Open Source framework in 2015, I was lucky enough to attend a workshop on neural networks – given by one of the AI scientists from Google Seattle – where I had my second experience with the platform. I was shocked by the simplicity of configuration and deployment, the NoOps concept and a Machine Learning / AI offering, without competition at the time.

Do Androids Dream of Electric Sheep? Philip K. Dick would have “hallucinated” with the electrical dreams of neural networks – powered by Tensor Flow.


The exam structure is the usual one in GCP exams: 2 hours and 50 questions, with a format directed towards scenario-type questions, mixing questions of great difficulty with simpler ones of medium-low difficulty.

In general, to choose the correct answer, you have to apply technical and business criteria. Therefore, a deep knowledge of the services from the technological point of view, as well as skill/experience, applies the business criteria contextually, depending on the question, type of environment, sector, application, etc …

Image #1, Data Lake, the ubiquitous architecture – Image owned by GCP
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