Databricks Databricks-Certified-Data-Engineer-Associate日本語 Exam Details & Actual Exam Questions
- Exam Code/Number: Databricks-Certified-Data-Engineer-Associate日本語
- Exam Name/Title: Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版)
- Certification Provider: Databricks
- Corresponding Certification: Databricks Certification
- Exam Questions: 234
- Updated On: Jul,15 2026
- Certification Level: Associate
Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版) Exam Questions
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Databricks Databricks-Certified-Data-Engineer-Associate日本語 Exam Overview:
| Certification Vendor: | Databricks |
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Associate |
| Exam Duration: | 90 minutes |
| Passing Score: | 70% |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60 |
| Related Certifications: | Databricks Certified Data Analyst Associate |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Price: | $200 USD |
| Sample Questions: | Databricks Databricks-Certified-Data-Engineer-Associate日本語 Sample Questions |
| Exam Way: | Online proctored or in-person testing center |
| Pre Condition: | Recommended: 6+ months of experience with Databricks and Apache Spark |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-associate |
Databricks Databricks-Certified-Data-Engineer-Associate日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components |
| Delta Lake Fundamentals | 20-25% | - Write to and read from Delta tables - Create and manage Delta tables - Explain Delta Lake features and benefits - Understand ACID transactions and time travel |
| Apache Spark Data Processing Fundamentals | 20-25% | - Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Create and use Spark DataFrames - Apply transformations and actions on DataFrames |
| Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing |
| Spark SQL and DataFrames | 15-20% | - Write and execute Spark SQL queries - Handle null values and data quality - Aggregate and group data - Join and union DataFrames |
| Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Implement incremental data processing |
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