Professional Data Engineers enable data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
The Professional Data Engineer exam assesses your ability to:
Length: 2 hours
Registration fee: $200 (plus tax where applicable)
Languages: English, Japanese
Exam format: Multiple choice and multiple select, taken remotely or in person at a test center. Locate a test center near you.
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.
Google Cloud Professional Data Engineer is an industry-recognized credential with practical assessments to prove professionals can design, build, and manage data solutions on Google Cloud. It is trusted by employers worldwide to identify skilled data engineers and appears in many data and analytics job listings.
The Google Cloud Professional Data Engineer certification requires candidates to pass one exam covering the core skills needed to design, build, and manage data solutions, emphasizing the technologies and practices professionals need to support data-driven cloud environments.
For Google Cloud Professional Data Engineer, the role reflects evolving demands in cloud data management:
Increased reliance on cloud-based data services and remote data pipelines
Strong focus on data ingestion, processing, analysis, and troubleshooting
Core skills include data infrastructure, machine learning, security, and automation
Working with multiple Google Cloud data services and platforms, understanding their use cases, and ensuring optimal performance
Data engineers must decide when to manage data solutions in-house or leverage managed services for efficiency, scalability, and cost optimization
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