Google Professional Data Engineer Online Course Launched Blog

As a Google Professional Data Engineer you will need to collect, modify, distribute, and monitor data in order to support data-driven decision-making. You will need to be proficient in developing, deploying and monitoring data processing systems. This includes security and compliance, scalability, efficiency, reliability, fidelity, portability, flexibility, and portability. You should also be able to use, deploy, and train existing machine learning models on an ongoing basis as a Data Engineer.
What is the exam looking for?
The Professional Data Engineer exam tests your ability to do the following:
Create data processing systems.
Data processing systems can be created and deployed.
Machine learning models need to be operationalized.
Ensure the quality of the solution
Google Cloud Certified Professional Data Engineer exam is the most difficult IT certification exam. Furthermore, the Google Professional-Data-Engineer exam is an expert level certification exam that will assist you in obtaining a high-ranking position in a reputable organisation. It is one the most difficult and prestigious IT certification exams. Passing this exam is however much more difficult. Google expects you to have a wide and deep knowledge base.
Exam Format
Let’s begin by going over the details of the Google Cloud Certified Professional Data Engineer certification exam. The Google Cloud Certified Professional Data Engineer exam takes two hours. The exam questions can be either multiple choice or multiple select. The candidate must score 70% to pass the exam. The exam is valid for two years and can be taken in English, Japanese Spanish, Portuguese, and Spanish. The exam costs $200 USD. Different exams have different requirements so it is important to understand the requirements of Professional Data Engineer.
These are the requirements for this exam:
The ideal candidate is scalable and efficient.
He or she should have the ability to design and monitor data processing systems that are security-focused.
A data engineer must be able to use and train existing machine learning models in a continuous manner.
Testpreptraining has created an online course that will help you learn the concepts and pass the Google Professional Data Engineer exam. Let’s take a look at the online course.
Online Course for Google Professional Data Engineer (GCP).
This course provides a complete introduction to the Google Cloud Platform. It includes 20 hours of content and 60 demos. Google Cloud Platform is perhaps the best cloud platform for high-end machine-learning applications, as it also offers TensorFlow which is a popular deep learning technology.
Course Features –
Certification material – This covers nearly all the material required to pass the Google Data Engineer or Cloud Architect certification tests.
Compute and Storage – AppEngine, Container Engine (aka Kubernetes), or Compute Engine offer compute and storage.
Managed Hadoop, Big Data – Dataflow, BigTable and BigQuery, Dataproc, Dataflow. Pub/Sub
TensorFlow on the Cloud explains the difference between deep learning and neural networks, and how they function. It also explains how neural networks can be trained.
Examples of DevOps tools include StackDriver monitoring, logging, and cloud deployment manager.
Identity and Access Management, Identity Aware Proxying and Identity-Aware Prxying are just a few examples of security features.
Networking – Virtual Private Clouds; shared VPCs, network and transport load balancing; VPN and Cloud Interconnect.
Hadoop Foundations: A look into the open-source cousins (Hadoop Spark, Pig, Hive and YARN).
This course will teach you how to fully understand and learn the following concepts:
Managed Hadoop apps are possible to be deployed