How I passed GCP Professional Data Engineer In 2020

Google Cloud Professional Data Engineer

How I passed GCP Professional Data Engineer In 2020

I’ve just passed my GCP professional data engineer exam in Oct 2020. A few people have reached out to me to ask for advice so I’m going to share my experience here and hope that this can help you nail the exam! Good luck 🙂

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Before diving into the exam prep…

Let’s have a look at how to prepare for it

Goal setting

Why am I taking this exam?”

What knowledge/experience I have that can be transferred across?”

When am I taking this exam? How much time am I willing to commit to study?”

What does GCP Professional Data Engineer exam cover?”

First thing first, these important questions because they help you plan your study schedule and set your objectives. As you see in the official exam guide , there are lot of things to learn — batch processing, streaming, Dataproc, Apache Beam, BigQuery, ML/AI,…., goal setting allows you to plan and priorities.

gcp exam goal

Recommended resources

Highly recommend it! It was like a massive playground which I let my curiosity take me to whatever I didn’t understand or found interesting. This helped me gain in-depth knowledge about each product. Real questions in guarantee part on gcp-examquestions very useful and ensure you can pass the exam at first time.

4. Medium

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There are some awesome articles about how to pass the exam here on Medium. I recommend using them as a quick overview. As products and features have changed overtime, official documentation is your best friend for the latest information.

5. Search for products on Google — Learn from real world use cases and practical tips.

6. Google Cloud Blog

7. Github — Search for the product name — Dataflow, BigTable, etc. to see the implementation and codes

Github Icon on gcp-examquestions

8. Google ML Crash Course — A refresher on ML concepts such as overfitting, variance, bias, etc.

My approach

1. Compare and contrast — Why/when would you use one over the other?

  • Cloud Storage vs. BigQuery Storage
  • Batch processing vs. Streaming processing
  • AutoML vs. ML API
  • Etc…

2. Hands-on practice

  • Cloud Shell commands
  • Build a real-time PubSub streaming pipeline for on-street parking in City of Melbourne — I picked on-street parking because it was open sourced and easy to access, you can use anything or even simulate your own streaming data!
  • Write/execute Python codes for Dataproc
  • Qwiklabs — I only did labs when I needed to see tangible results or the flow of execution.

3. Practice exam more and more. 

  • the Guarantee Part is really helpful with actual exam questions. Ensure you can pass the exam.

Topics to study

GoogleCloud Docs

Source: Medium

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