Certified Data Engineer Professional
- Product Code: cdep
- Exam Questions: 109
- Vendor: databricks
$9.99
$12.99- Updated At: Nov 14, 2024
Certified Data Engineer Professional
Certified Data Engineer Professional is a certification program offered by Databricks, focusing on Apache Spark and Big Data engineering.
Focus
The Certified Data Engineer Professional certification focuses on Data Engineering, Databricks, Apache Spark, Big Data
Exam Duration
The Certified Data Engineer Professional exam duration is 3 hours
Num of Question
The exam contains a total of 77
Exam Fee
The exam fee is $395 USD.
Exam Validity
The CCRN-Pediatric certification is valid for 3 years.
Prior Certification
- Databricks Certified Developer Professional
Exam Format
The Certified Data Engineer Professional exam is a Multi-Choice questions
Passing Conditions
To pass Certified Data Engineer Professional exam, applicants must achieve a minimum score of Score of 720 (86%)
Who Should Take
Data engineers, data scientists, and professionals interested in data engineering and architecture
- Data Engineer
- Data Architect
- Data Scientist
- Big Data Engineer
Prerequisites
2-3 years of experience in data engineering, architecture, and design
Or can substitute with certifications:
- Databricks Certified Developer Professional
- Databricks Certified Associate Engineer Professional
Certification Process
- Step 1: Prepare for the exam by learning Databricks and Apache Spark features
- Step 2: Register for the exam on the Databricks website
- Step 3: Take the exam and score a minimum of 720 (86%)
Domains with Weights
- Domain 1: Data Engineering Fundamentals (30%): Designing, building, and deploying data engineering solutions using Databricks and Apache Spark
- Domain 2: Data Engineering Practices (25%): Implementing data engineering best practices, working with cloud storage, and managing data pipelines
- Domain 3: Databricks Advanced Features (20%): Working with Databricks features such as Delta Lake, Hive, and MLflow
- Domain 4: Data Architecture and Design (25%): Designing and implementing scalable data architectures using Databricks and Apache Spark
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