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Google Professional Certifications Professional Data Engineer

Product Description

Exam Code:Professional Data Engineer

Exam Name:Google Certified Professional – Data Engineer

Q&A:331 Q&As

Updated: 04-27-2024

Number:

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ExamGood Professional-Data-Engineer Exam Features

A Professional Data Engineer makes data usable and valuable for others by collecting, transforming, and publishing data. This individual evaluates and selects products and services to meet business and regulatory requirements. A Professional Data Engineer creates and manages robust data processing systems. This includes the ability to design, build, deploy, monitor, maintain, and secure data processing workloads.

The Professional Data Engineer exam assesses your ability to:

Design data processing systems

Ingest and process data

Store the data

Prepare and use data for analysis

Maintain and automate data workloads

About this certification exam

Length: Two hours

Registration fee: $200 (plus tax where applicable)

Languages: English, Japanese.

Exam format: 50-60 multiple choice and multiple select questions

Exam delivery method:

a. Take the online-proctored exam from a remote location.

b. Take the onsite-proctored exam at a testing center

Prerequisites: None

Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.

Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.

Professional Data Engineer Exam Sections

Section 1: Designing data processing systems

Section 2: Ingesting and processing the data

Section 3: Storing the data

Section 4: Preparing and using data for analysis

Section 5: Maintaining and automating data workloads

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