Databricks Certified Professional Data Engineer

Databricks Certified Professional Data Engineer

Demonstrate skills in designing, implementing, and maintaining data engineering solutions on the Databricks platform.

Focus

The Databricks Certified Professional Data Engineer certification focuses on Data Engineering

Exam Duration

The Databricks Certified Professional Data Engineer exam duration is N/A

Num of Question

The exam contains a total of N/A

Exam Fee

The exam fee is N/A.

Exam Validity

The CCRN-Pediatric certification is valid for N/A.

Prior Certification

• N/A

Exam Format

The Databricks Certified Professional Data Engineer exam is a Multiple Choice and Hands-on questions

Passing Conditions

To pass Databricks Certified Professional Data Engineer exam, applicants must achieve a minimum score of N/A

Who Should Take

Data Engineering and Data Science professionals, especially those working with the Databricks platform.

• Data Engineer
• Data Architect
• Data Scientist

Prerequisites

1-2 years of experience in data engineering, data science, or a related field.

Or can substitute with certifications:

• Equivalent number of hours from Databricks Learning Platform, or equivalent experience with other cloud-native data engineering platforms.

Certification Process

• Register for the exam on the Databricks website or on a Pearson VUE test center.
• Prepare for the exam using official study materials, online courses, and hands-on practice on a Databricks environment.
• Pass the exam with a score of 70% or higher.

Domains with Weights

• Design and implement scalable data engineering solutions (40%): Design and implement data pipeline solutions for data ingestion, processing, and storage on Databricks. This includes choosing optimal use cases, implementing data quality checks, and handling data security. Weights for this domain will be 40%
• Build, deploy, and manage cloud-native data engineering workloads (30%): Design, implement, and manage cloud-native data engineering workloads on Databricks using cloud computing and containerization technologies. This includes choosing optimal deployment strategies, automating deployment and scaling, and handling workloads across multiple clusters. Weights for this domain will be 25% (20% on AWS and 5% on Azure).
• Ensure data quality, security, and governance (15%): Implement data quality checks, ensure data security, and maintain data governance policies within Databricks. This includes data validation, data encryption, and auditing.
• Optimize data engineering performance and cost (15%): Create efficient and well-optimized Databricks architecture to reduce cost and improve performance. This includes optimizing data processing workflows, choosing efficient storage options, and implementing cost-efficient scaling strategies.
• Implement data lake and data warehouse for enterprise data architecture (5%): Implement data lake and data warehouse solutions on Databricks. This includes designing data lake architecture, implementing data governance policies, and creating data warehouse solutions for decision-making.