best practice Tests for Professional Data Engineer on Google Cloud Platform Certification 2021
What you will learn
Practice Tests for Google Cloud Platform Certification
Practice tips for Google Cloud Platform Certification
Practice same Exam for Google Cloud Platform Certification
Discover all tricks in exam certification
Practice Tests for Google Cloud Platform Certification
Practice tips for Google Cloud Platform Certification
Practice same Exam for Google Cloud Platform Certification
Discover all tricks in exam certification
Description
If you are going for Google Cloud Certified Professional Data Engineer Exam you must follow a track. Therefore, the article is Apt to help you in preparing for the Google Certified Professional Data Engineer Exam.
To sum up, everything you need to know is a list of following:
- Firstly, Current Google Certified Professional Data Engineer certification
- Its values
- Requirements
- Exam guide and resources
Without further a do, let’s get ahead step by step!
Who is a Data Engineer?
Likewise technology, with the advent of “big data,” the area of responsibility has changed. Most companies store their data in variety of formats across database. This is where data engineer comes in.
Data engineer is typically in charge of managing data workflows, pipelines, and ETL processes. Moreover, a data engineer transforms data into a useful format for analysis.
“A scientist can discover a new star, but he cannot make one. He would have to ask an engineer to do it for him.”
Understanding Google Cloud Certified Professional Data Engineer Certification
Before scrolling down the article to find details of the exam, the main question is why get certification in Google Cloud Data Engineer. The answer to this question is cloud computing.
In other words, cloud computing is a recent trend which is here, and here to stay. You can refer the points below to get much clearer picture:
- Firstly, there has been significant growth in the computer industry. This growth will continue for the next five years.
- Secondly, the amount of data being introduced is expanding faster. For instance, 90 percent of the data is created in the last 2 years.
- Thirdly, the ability to tame the data into usable information is becoming more complex and requires new skills and abilities.
- In conclusion, a data engineer fits clearly into this area, which further encourages to certification in the same.
Exam Overview
Google Data Engineer Certification is befitting for a candidate who wishes to become a data engineer. Therefore, to become a data engineer, the candidate has to pass the certification exam.
The Professional Data Engineer exam evaluates your ability to:
Course Outline
After getting a better understanding of the required details, let’s discuss the exam outline. Let’s take a glance at the topics that needed to be covered for the exam and you need to pay focus on with the Google Cloud Certified Professional Data Engineer Syllabus:
Designing data processing systems
- Firstly, selecting appropriate storage technologies.
- Then, making designs for data pipelines.
- Thirdly, designing a data processing solution.
- Lastly, migrating data warehousing and data processing.
Building and operationalizing data processing systems
- Firstly, building and operationalizing storage systems.
- Then, building and operationalizing pipelines with processing infrastructure
Operationalizing machine learning models
- Firstly, leveraging pre-built ML models as a service.
- Then, deploying an ML pipeline
- Thirdly, choosing the appropriate training and serving infrastructure.
- Lastly, measuring, monitoring, and troubleshooting machine learning models.
If you are going for Google Cloud Certified Professional Data Engineer Exam you must follow a track. Therefore, the article is Apt to help you in preparing for the Google Certified Professional Data Engineer Exam.
To sum up, everything you need to know is a list of following:
- Firstly, Current Google Certified Professional Data Engineer certification
- Its values
- Requirements
- Exam guide and resources
Without further a do, let’s get ahead step by step!
Who is a Data Engineer?
Likewise technology, with the advent of “big data,” the area of responsibility has changed. Most companies store their data in variety of formats across database. This is where data engineer comes in.
Data engineer is typically in charge of managing data workflows, pipelines, and ETL processes. Moreover, a data engineer transforms data into a useful format for analysis.
“A scientist can discover a new star, but he cannot make one. He would have to ask an engineer to do it for him.”
Understanding Google Cloud Certified Professional Data Engineer Certification
Before scrolling down the article to find details of the exam, the main question is why get certification in Google Cloud Data Engineer. The answer to this question is cloud computing.
In other words, cloud computing is a recent trend which is here, and here to stay. You can refer the points below to get much clearer picture:
- Firstly, there has been significant growth in the computer industry. This growth will continue for the next five years.
- Secondly, the amount of data being introduced is expanding faster. For instance, 90 percent of the data is created in the last 2 years.
- Thirdly, the ability to tame the data into usable information is becoming more complex and requires new skills and abilities.
- In conclusion, a data engineer fits clearly into this area, which further encourages to certification in the same.
Exam Overview
Google Data Engineer Certification is befitting for a candidate who wishes to become a data engineer. Therefore, to become a data engineer, the candidate has to pass the certification exam.
The Professional Data Engineer exam evaluates your ability to:
Course Outline
After getting a better understanding of the required details, let’s discuss the exam outline. Let’s take a glance at the topics that needed to be covered for the exam and you need to pay focus on with the Google Cloud Certified Professional Data Engineer Syllabus:
Designing data processing systems
- Firstly, selecting appropriate storage technologies.
- Then, making designs for data pipelines.
- Thirdly, designing a data processing solution.
- Lastly, migrating data warehousing and data processing.
Building and operationalizing data processing systems
- Firstly, building and operationalizing storage systems.
- Then, building and operationalizing pipelines with processing infrastructure
Operationalizing machine learning models
- Firstly, leveraging pre-built ML models as a service.
- Then, deploying an ML pipeline
- Thirdly, choosing the appropriate training and serving infrastructure.
- Lastly, measuring, monitoring, and troubleshooting machine learning models.