• Exam Code: Databricks-Certified-Professional-Data-Engineer
  • Exam Name: Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version)
  • Certification Provider: Databricks
  • Corresponding Certification:Databricks Certification
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Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Professional Data Engineer Exam
Exam Number:Databricks-Certified-Professional-Data-Engineer
Passing Score:70%
Available Languages:English
Related Certifications:Databricks Certified Data Engineer Associate
Exam Price:USD 200
Real Exam Qty:60-70
Exam Format:Multiple select, Scenario-based questions, Multiple choice
Exam Duration:120 minutes
Certificate Validity Period:2 years
Recommended Training:Databricks Academy
Exam Registration:Databricks Certification Portal
Sample Questions:Databricks Databricks-Certified-Professional-Data-Engineer Korean Sample Questions
Exam Way:Online proctored exam
Pre Condition:Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform.
Official Syllabus URL:https://www.databricks.com/learn/certification

Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:

SectionObjectives
Topic 1: Production Pipelines and Orchestration- Pipeline reliability and fault tolerance
- Automate ETL pipelines and scheduling
- Build and manage workflows using Databricks Jobs
Topic 2: Security, Governance, Monitoring, and Optimization- Implement Unity Catalog governance and access control
- Cost optimization and performance tuning
- Monitor and optimize Spark workloads
Topic 3: Data Ingestion and Transformation- Handle batch and streaming data pipelines
- Ingest data using Apache Spark and Databricks
- Transform and clean datasets using Spark SQL and DataFrame APIs
Topic 4: Data Modeling and Storage- Design scalable data lakehouse architectures
- Schema evolution and data partitioning strategies
- Delta Lake table design and optimization

What Candidates Ask Before Databricks-Certified-Professional-Data-Engineer Korean

Think of the Databricks-Certified-Professional-Data-Engineer Korean exam as Databricks asking for proof, not promises: pass it, and you hold the Databricks Certified Professional Data Engineer certification, a credential at the Professional level. The questions are built to measure applied skill across the Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) objectives — the kind employers hire for. It also sits within a broader certification family covering Databricks Certified Data Engineer Associate, so one exam's effort keeps paying into the next.

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Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) Sample Questions:

프로덕션 클러스터는 3개의 실행기 노드를 가지며 드라이버와 실행기에 동일한 가상 머신 유형을 사용합니다.
이 클러스터의 Ganglia 메트릭을 평가할 때, 드라이버에서 실행되는 코드로 인해 발생하는 병목 현상을 나타내는 지표는 무엇일까요?

  • A. 전체 클러스터 CPU 사용률은 약 25%입니다.
  • B. 총 디스크 공간은 일정하게 유지됩니다.
  • C. 5분 평균 부하량이 일정하게 유지됩니다/평탄하게 유지됩니다
  • D. 네트워크 I/O는 절대 급증하지 않습니다.
  • E. 수신 바이트는 초당 8천만 바이트를 초과하지 않습니다.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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프로덕션 환경에 구조화된 스트리밍 작업을 예약할 때, 쿼리 실패 시 자동으로 복구되고 비용을 낮게 유지하는 구성은 무엇입니까?

  • A. 클러스터: 새로운 직업 클러스터;
    재시도 횟수: 무제한;
    최대 동시 실행 수: 1
  • B. 클러스터: 새로운 직업 클러스터;
    재시도 횟수: 없음;
    최대 동시 실행 수: 1
  • C. 클러스터: 새로운 직업 클러스터;
    재시도 횟수: 무제한;
    최대 동시 실행 횟수: 무제한
  • D. 클러스터: 기존 범용 클러스터;
    재시도 횟수: 무제한;
    최대 동시 실행 수: 1
  • E. 클러스터: 기존 다목적 클러스터;
    재시도 횟수: 없음;
    최대 동시 실행 수: 1
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Correct Answer: A  🗳️

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사용자는 DLT 기대치를 사용하여 파생 테이블 보고서에 원본의 모든 레코드가 포함되어 있는지, 그리고 테이블 validation_copy에 포함된 레코드가 있는지 검증하려고 합니다.
사용자는 보고서 테이블 정의에 기대값을 추가하는 방식으로 이를 시도했지만 실패했습니다.
DLT 기대값을 사용하여 이 테이블에 예상되는 모든 레코드가 있는지 검증할 수 있는 접근 방식은 무엇일까요?

  • A. validation_copy 테이블과 report 테이블을 왼쪽 외부 조인하는 임시 테이블을 정의하고, report 테이블의 키 값 중 null이 없어야 한다는 조건을 정의합니다.
  • B. validation_copy 테이블과 report 테이블을 왼쪽 외부 조인하는 뷰를 정의하고, DLT에서 report 테이블에 대한 기대치에 이 뷰를 참조합니다.
  • C. validation_copy, report, report 테이블을 대상으로 왼쪽 외부 조인을 수행하고, 그 결과를 DLT 기대값과 비교하여 report 테이블을 검증하는 함수를 정의하십시오.
  • D. 두 테이블에 대해 왼쪽 외부 조인을 수행하는 SQL 사용자 정의 함수(UDF)를 정의하고, 이 함수가 보고서 테이블에 대한 DLT 기대치에서 보고서 키 값에 대해 null 값을 반환하는지 확인합니다.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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다음 중 Delta Lake Auto Compaction을 설명하는 문장은 무엇입니까?

  • A. 작업 클러스터가 종료되기 전에 가장 최근 작업 중에 수정된 모든 테이블에 대해 최적화가 실행됩니다.
  • B. 데이터는 메모리에 직접 커밋하는 대신 메시징 버스에 대기열에 저장됩니다. 작업이 완료되면 메시징 버스에 있는 모든 데이터가 한 번에 커밋됩니다.
  • C. 최적화된 쓰기는 디렉터리 파티션 대신 논리 파티션을 사용합니다. 파티션 경계는 메타데이터에만 표현되므로 더 적은 수의 작은 파일이 기록됩니다.
  • D. 쓰기 작업이 완료된 후 비동기 작업이 실행되어 파일을 추가로 압축할 수 있는지 확인합니다. 압축할 수 있는 경우 기본값인 128MB를 목표로 최적화 작업이 실행됩니다.
  • E. 쓰기 작업이 완료된 후 비동기 작업이 실행되어 파일을 추가로 압축할 수 있는지 확인합니다. 압축할 수 있는 경우 기본값인 1GB를 목표로 최적화 작업이 실행됩니다.
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Correct Answer: D  🗳️

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개발자가 Databricks 리포지토리에 대한 자격 증명을 성공적으로 구성하고 원격 Git 리포지토리를 복제했습니다.
해당 사용자는 메인 브랜치를 수정할 권한이 없습니다. 현재 해당 사용자의 작업 공간에는 메인 브랜치만 표시됩니다.
Response를 사용하여 원격 Git 저장소의 커밋에서 변경 사항을 가져오고, 가져온 변경 사항이 나타난 브랜치로 변경 사항을 푸시할 수 있습니다.

  • A. Repos를 사용하여 새 브랜치를 생성하고 모든 변경 사항을 커밋한 다음 원격 Git 저장소에 변경 사항을 푸시합니다.
  • B. 모든 차이점을 병합하기 위해 리포지토리를 사용하고 원격 리포지토리로 풀 리퀘스트를 생성합니다.
  • C. 리포지토리를 사용하여 원격 리포지토리를 포크하고 모든 변경 사항을 커밋한 다음 소스 리포지토리에 풀 리퀘스트를 생성합니다.
  • D. Repos를 사용하여 모든 차이점을 병합하고 원격 저장소로 풀 리퀘스트를 생성합니다.
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Correct Answer: A  🗳️

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