How I Passed The GCP Professional Data Engineer Certification(In 1st attempt)
I passed the Google Cloud Professional Data Engineer exam on the first attempt on 27th September. Let me share my preparation with you all for your reference.
Originally published at https://asyncq.com
Table of Contents
- My Previous Experience with GCP
- Current work with GCP
- My Preparation For Exam
- Practice Test
- One important Tip
Youtube Video :
If you prefer video format : https://youtu.be/VbAnrhwFjP0
My Previous Experience with GCP
I started my GCP journey in 2018 when I was working on Kafka and Kubernetes. I was so happy to see my first distributed software installed and communicating with each other. (It was a pleasure when you deploy distributed software like Kafka in a cluster environment)
During that time I learned about streaming technology, GCP Compute Engine, Docker, Kubernetes.
Current Work with GCP
My current work is to build a data platform on GCP. I explored various products like BigQuery, Data Studio, Cloud Spanner, Cloud BigTable, Google cloud storage, Cloud Composer, KMS, Dataflow, and apache beam. I tried POC and tried to understand each product and finalize the design for the business requirements.
My Preparation for Exam
Although I worked with many data analytics products on GCP, there were some products I didn't explore like PubSub, Dataproc, Data fusion, Data prep, and Machine Learning products like Kubeflow, Machine Learning Platform.
Hence I needed some online courses which could give me a basic understanding of the product.
1: Video Courses I Completed
List of courses I completed on Pluralsight:
Architecting Big Data Solutions Using Google Bigtable
Beginner Dec 4, 2018 2h 2m Description Bigtable is Google's proprietary storage service that offers extremely fast read…
Creating and Administering Google Cloud Spanner Instances
Course Overview Hi, my name is Vitthal Srinivasan, and I'd like to welcome you to this course on Creating and…
Creating and Administering Google Cloud SQL Instances
Cloud SQL is the Google Cloud Platform's managed SQL service which offers powerful and simple RDBMS functionality…
Leveraging Google Cloud Firestore for Realtime Database Solutions
Course Overview Hi, my name is Janani Ravi, and welcome to this course on Leveraging Google Cloud Firestore for…
Architecting Stream Processing Solutions Using Google Cloud Pub/Sub
This course is about working with Pub/Sub, including creating and managing topics, subscriptions, and publishing…
The Building Blocks of Hadoop - HDFS, MapReduce, and YARN
Processing billions of records requires a deep understanding of distributed computing. In this course, you'll get…
Architecting Serverless Big Data Solutions Using Google Dataflow
Dataflow represents a fundamentally different approach to Big Data processing than computing engines such as Spark…
Google Cloud Functions: Getting Started
Serverless technologies are taking the developer world by storm because they allow you to push out code quickly and…
Building Pipelines for Workflow Orchestration Using Google Composer
Cloud Composer is a fully managed workflow orchestration service that allows creation, scheduling, and monitoring of…
Applying Machine Learning to your Data with GCP
Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales…
Smart Analytics, Machine Learning, and AI on GCP
Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their…
Understanding the Foundations of TensorFlow
Beginner Jul 26, 2017 2h 44m Description In this course, Understanding the Foundations of TensorFlow, you'll learn the…
Overall Data Platform
Modernizing Data Lakes and Data Warehouses with GCP
The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each…
Above courses were very helpful to set the base for understanding of data engineering on GCP.
2: Product Documentation
Next step is took was to go through the documentation of each product. This was helpful in understanding detail concepts . Going through entire documentation looked too much initially but this is very important in order to not miss out on certain important concepts. I decided to use 1 hour of my day to go through the documentation. https://cloud.google.com/docs
3: GCP Qwiklabs
I practiced data engineering quest on GCP Qwiklabs which is good way to perform hands-on. https://bit.ly/35v0TZ1
4: Final Steps
- I referred this blog very useful during last week preparation in order to avoid if i am missing any concepts.
Google Cloud Certified Professional Data Engineer - 2019 Updated exam
Starting 29th of March Google Cloud Professionals attempting Data Engineer certification will be subject to the latest…
- This course from GCP on Pluralsight was also good for last week preparation.
Preparing for the Google Cloud Professional Data Engineer Exam
This course helps prospective candidates structure their preparation for the Professional Data Engineer exam. The…
- Google Cloud Provide Sample Question, which I practiced before exam: https://cloud.google.com/certification/sample-questions/data-engineer
- Exam Topics: https://www.examtopics.com/exams/google/professional-data-engineer/view/4/
- Test prep training: https://www.testpreptraining.com/google-cloud-certified-professional-data-engineer-free-practice-test
One important Tip
Understanding open source alternatives to each GCP product would be very helpful in exam, since questions include comparison with open source alternatives.
For example, I studied GCP PubSub, but I also learnt about Apache Kafka . This is important because In the exam you would often ask about questions on comparison and alternatives to choose from. Hence If learning about Google Dataflow is mandatory to pass the exam but understanding Apache Spark and Flink is also important to become data engineer.
Thanks for your time!