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This Google certified professional data engineer practice test 2021 is a sum of modules covering design systems that ingest large volume data, effective storage of data, efficient processing and analysis of data. The test series also includes questions that revolve arounds the utilization of tools for visualization, reporting and machine learning.
Post the practice test the applicants will have a sound knowledge of how to build, tune and monitor a high performance data system.
Cloud is an exponentially growing platform and we are ought to deliver practice tests that meet the time requirements.
Note:- Exam format: Multiple choice and multiple select taken remotely or in person at a test center.
1 Sets of practice test- with 10 questions for Free
3 Sets of Practice Exam - with 50 Questions each sets
Each practice questions very- very important for the exam and has the same format, style, time limit and passing score as the real Google Cloud Data Engineer Certification exam (75 min to answer 50 questions)
All questions are unique and important with Explanations for understanding the questions the, 100% scenario-based and conform to the latest Google Cloud Professional Data Engineer exam blueprint.
150+ Test Questions
2 Practice Tests
Detailed Explanation of correct and incorrect choice
Reply to all queries/doubts at the earliest
What do our Google certified professional data engineer training test offers?
A detailed set of questions focusing the pattern of the ultimate certification examination.
Insight of the industrial experts with an experience of 10+ years in the industry.
Complete performance analytical report by the end of the exam.
A well planned preparation planner
Who is this course for?
Developers who want to enhance their knowledge of Google Cloud
Individuals who need a hike in their regular jobs.
Individuals who are pursuing Google Cloud Certification.
IITians who are preparing for jobs in the similar field
Developers who want to make effective use of the google cloud console.
Designing data processing systems.
Designing data pipelines.
Designing a data processing solution.
Migrating data warehousing and data processing.
Building and operationalization data processing systems
Building and operationalization storage systems.
Building and operationalization pipelines.
Building and operationalization processing infrastructure.
Operationalization machine learning
Leveraging pre-built ML models as a service.
Deploying an ML pipeline.
Designing for security and compliance.
Ensuring scalability and efficiency.
Ensuring reliability and fidelity.
Abhishek is CEO at Skugal and Code For Startups , CTO at Etheralabs. He leads the development efforts for Skugal, Code For Startups, Etheralabs, and as a software development engineer with expertise in building products using emerging technologies, Abhishek is responsible for the ongoing advancement of All these three companies multiple Big Data and Machine Learning development projects. Abhishek likes rock climbing and also reads ancient books.