Recipe for Success: Google Cloud Professional — Data Engineer Certification with just 4 days of preparation?

Saikrishna Pujari
3 min readNov 22, 2019

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So, What’s cooking these days? : Cloud Technologies, right?

This article contains my recipe for success in clearing the GCP-Data Engineer certification with just four days of preparation.

Firstly, sorry for the overstated title, but follow the article to get the context.

FYI: This is not an easy recipe to make, some dedication is needed.

Recipe History:

I have been working as a Data Engineer for over three years now, mostly on Hadoop stack and Spark. In my quest of learning industry trending technologies in Data Engineering space, I thought what’s next? So as the industry is moving towards cloud solutions, I have planned to take on GCP — Data Engineer certification.

Recipe Ingredients:

Here I am listing the major ingredients (services/components) in the order of importance.

  1. Big Query
  2. Pub/Sub
  3. Dataflow
  4. BigTable
  5. Dataproc
  6. IAM
  7. ML
  8. Data Studio
  9. Cloud SQL
  10. Cloud Spanner
  11. Apache Beam
  12. Stackdriver
  13. Cloud composer
  14. Cloud scheduler

Recipe Method:

As I understood, Google wants us to know all their major service offerings in Data engineering space and how they can be mixed to create a recipe for data engineering success! :)

I feel the USP of Google Cloud Platform is that they are opening their world-scale infrastructure for their cloud customers, it’s the same technologies that power the mighty Google.

The exam is designed to test the breadth not much of the depth of each particular service, It mostly is a Data Architect kind of an exam.

What really helped me in clearing the exam with just four days of dedicated preparation is my past experience in working with Hadoop tools and Spark, I can see the direct correlation between open source tools vs cloud services like BigTable-HBase, BigQuery-Hive, Pub/Sub — Kafka, etc., If you can get this understanding it can really help you.

My recipe strategy in steps below:

Step 1: Enrolled in Google Cloud Data Engineering specialization, completed the fundamentals course and Qwiklabs practice.

Step 2: Taken the official practice test, I have scored around 70% here, but I have analyzed all the wrong answers, they also provide the explanations and links to refer, this helped a lot on what their expectations from you.

Step 3: Referred to Google Official documentation of the above-mentioned products for their recommended best practices, I have seen quite a few questions on best practices.

Step 4: Followed blogs written by others who cleared the exam, which has helped me a lot in narrowing down my focus to specific topics/question patterns.

Step 5: Little bit of luck as well, This is a tricky exam with all 50 questions are like 50 different scenarios, I have marked around 20 for the review, So it is all about preparation (80%) plus luck (20%).

Recipe Result:

Recipe Sources:

We learn from each other, So here are a few of the blogs I have referred to.

  1. https://medium.com/@sathishvj/notes-from-my-google-cloud-professional-data-engineer-exam-530d11966aa0
  2. https://deploy.live/blog/google-cloud-certified-professional-data-engineer/
  3. https://towardsdatascience.com/passing-the-google-cloud-professional-data-engineer-certification-87da9908b333
  4. https://medium.com/nooblearning/2019-google-cloud-professional-data-engineer-certification-exam-6a5d6581e507

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Saikrishna Pujari

Sr. Spark Solutions Engineer @ Databricks | Lead Data Engineer | Databricks Certified Spark Developer | GCP Data Engineer | Azure Data Engineer