Databricks Databricks-Certified-Data-Engineer-Professional Korean : Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version)
Exam Code: Databricks-Certified-Data-Engineer-Professional-KR
Exam Name: Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version)
Updated: Oct 08, 2026
Q & A: 250 Questions and Answers
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Databricks Databricks-Certified-Data-Engineer-Professional Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing | 28% | - Spark SQL - Data Transformation - ETL Pipelines - Structured Streaming |
| Topic 2: Data Quality and Governance | 12% | - Data Quality - Governance - Data Lineage |
| Topic 3: Monitoring and Troubleshooting | 16% | - Monitoring - Performance Optimization - Troubleshooting |
| Topic 4: Databricks Lakehouse Platform | 24% | - Unity Catalog - Lakehouse Architecture - Delta Lake - Data Management |
| Topic 5: Data Modeling and Storage | 20% | - Data Modeling - Storage Optimization - File Formats |
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) Sample Questions:
Databricks CLI는 job_id 매개변수를 전달하여 기존 작업을 실행하는 데 사용됩니다. 작업 실행 요청이 성공적으로 제출되었다는 응답에는 run_id 필드가 포함됩니다.
다음 중 이 항목 옆의 숫자가 나타내는 바를 설명하는 문장은 무엇입니까?
- A. 워크스페이스에서 작업 정의가 실행된 횟수입니다.
- B. 이 필드에는 job_id가 반환됩니다.
- C. 워크스페이스에서 실행된 총 작업 수입니다.
- D. 새로 시작된 실행의 전역적으로 고유한 ID입니다.
- E. 작업 ID와 해당 작업이 수행된 횟수를 연결하여 반환합니다.
Correct Answer: D 🗳️
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아래의 각 구성은 클러스터당 총 400GB의 RAM, 총 160개의 코어, 그리고 VM당 하나의 Executor만 갖는다는 점에서 동일합니다.
완료가 반드시 보장되어야 하는 매우 오래 걸리는 작업이 주어졌을 때, 하나 이상의 VM 장애가 발생하더라도 작업 완료를 보장할 수 있는 클러스터 구성은 무엇일까요?
- A. - 총 VM 수: 1
- 실행기당 400GB
- 160 코어/실행기 - B. - 총 VM 수: 16
- 실행기당 25GB
- 10개 코어/실행기 - C. - 총 VM 수: 8
- 실행기당 50GB
- 20개 코어/실행기 - D. - 총 VM 수: 2
- 실행기당 200GB
- 80개 코어/실행기 - E. - 총 VM 수: 4
- 실행기당 100GB
- 40개 코어/실행기
Correct Answer: B 🗳️
주니어 개발자가 자신의 노트북에 있는 코드가 개발 환경에서 올바른 결과를 생성하지 못한다고 불평했습니다. 공유된 스크린샷을 보면, 해당 개발자는 Databricks 리포지토리를 사용하여 버전 관리되는 노트북을 사용하고 있지만, 이전 로직이 포함된 개인 브랜치를 사용하고 있는 것으로 나타났습니다. 원하는 브랜치인 dev-2.3.9가 브랜치 선택 드롭다운 메뉴에 나타나지 않습니다.
어떤 접근 방식을 사용하면 이 개발자가 이 노트북의 현재 로직을 검토할 수 있을까요?
- A. Repos를 사용하여 dev-2.3.9 브랜치를 체크아웃하고 현재 브랜치와의 충돌을 자동으로 해결합니다.
- B. 리포지토리를 사용하여 풀 리퀘스트를 생성하고, Databricks REST API를 사용하여 현재 브랜치를 dev-2.3.9로 업데이트합니다.
- C. 모든 변경 사항을 원격 Git 저장소의 메인 브랜치에 병합하고 저장소를 다시 클론합니다.
- D. Repos를 사용하여 원격 Git 저장소에서 변경 사항을 가져오고 dev-2.3.9 분기를 선택합니다.
- E. Repos를 사용하여 현재 브랜치와 dev-2.3.9 브랜치를 병합한 다음, 원격 저장소와 동기화하기 위한 풀 리퀘스트를 생성합니다.
Correct Answer: D 🗳️
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Spark에 데이터를 입력할 때 Spark 파티션 크기에 직접적인 영향을 미치는 구성 매개변수는 무엇입니까?
- A. spark.sql.autoBroadcastJoinThreshold
- B. spark.sql.adaptive.coalescePartitions.minPartitionNum
- C. spark.sql.adaptive.advisoryPartitionSizeInBytes
- D. spark.sql.files.maxPartitionBytes
- E. spark.sql.files.openCostInBytes
Correct Answer: D 🗳️
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컴퓨팅 리소스를 가장 많이 소비하는 사용자를 파악하기 위해 데이터 엔지니어링 팀은 리소스 활용도 및 비용 관리를 개선하기 위해 Databricks 워크스페이스 내 사용량을 모니터링해야 합니다.
팀은 작업 공간 활동에 대한 자세한 정보를 얻기 위해 Unity 카탈로그의 시스템 카탈로그에서 사용할 수 있는 Databricks 시스템 테이블을 사용하기로 결정했습니다. 이를 위해 팀은 시스템 카탈로그에서 어떤 SQL 쿼리를 실행해야 할까요?
- A. sku_name을 선택합니다,
usage_metadata.run_name AS user_email,
사용량 합계를 총 DBUS로 계산
시스템.청구.사용에서
사용자 이메일과 제품명(sku_name)을 기준으로 그룹화합니다.
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개 - B. sku_name을 선택합니다,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
시스템.청구.사용에서
사용자 이메일과 제품명(sku_name)을 기준으로 그룹화합니다.
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개 - C. sku_name을 선택합니다,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
시스템.청구.사용에서
사용자 이메일과 제품명(sku_name)을 기준으로 그룹화합니다.
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개 - D. SELECT identity_metadata.run_as AS user_email,
사용량 합계를 총 DBUS로 계산
시스템.청구.사용에서
사용자 이메일별로 그룹화
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개
Correct Answer: D 🗳️
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