Google Certified Professional Data Engineer Exam

Google Certified Professional Data Engineer Exam

Demonstrate expertise in designing, building, and managing scalable, secure, and reliable data engineering systems on Google Cloud.

Focus

The Google Certified Professional Data Engineer Exam certification focuses on Data Engineering on Google Cloud

Exam Duration

The Google Certified Professional Data Engineer Exam exam duration is 180 minutes

Num of Question

The exam contains a total of 100

Exam Fee

The exam fee is $200 USD.

Exam Validity

The CCRN-Pediatric certification is valid for 2 years.

Prior Certification

• Google Cloud - Cloud Developer, Cloud Engineer - Professional Certificate, Google Cloud - Certified Professional Cloud Developer certification, or equivalent experience

Exam Format

The Google Certified Professional Data Engineer Exam exam is a Multiple-choice questions

Passing Conditions

To pass Google Certified Professional Data Engineer Exam exam, applicants must achieve a minimum score of Score of 720 passing score (out of 1000 possible points)

Who Should Take

Data engineers, solution architects, technical program managers, and anyone seeking to enhance their skills in designing and managing data engineering systems on Google Cloud.

• Data Engineer
• Solution Architect
• Technical Program Manager

Prerequisites

At least 24 months of relevant hands-on programming experience, including experience with large-scale data processing and storage systems.

Or can substitute with certifications:

• No substitutions are available for this exam

Certification Process

• Step 1: Review the exam topics and objectives.
• Step 2: Get hands-on experience with Google Cloud Platform (GCP) products and services.
• Step 3: Prepare for the exam using study materials and practice questions.
• Step 4: Schedule the exam and take it within 2 years of passing the certification.

Domains with Weights

• Domain 1: Data Engineering Systems (30%): Design, build, and manage scalable, secure, and reliable data engineering systems on Google Cloud, including data ingestion, processing, and storage.
• Domain 2: Data Governance and Security (25%): Implement data governance and security best practices, including data encryption, access control, and auditing, to ensure data compliance and security.
• Domain 3: Data Integration and Pipelines (20%): Design, implement, and manage data integration and pipeline solutions on Google Cloud, including data warehousing, data lakes, and big data processing.
• Domain 4: Data Warehousing and Big Data (25%): Design and implement data warehousing and big data solutions on Google Cloud, including data modeling, ETL, and data visualization.