Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) - Databricks-Certified-Data-Engineer-Professional Korean

Databricks Databricks-Certified-Data-Engineer-Professional Korean Actual PDF
  • 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:
SectionWeightObjectives
Topic 1: Data Sharing and Federation5%- Use Delta Sharing for secure data sharing
- Implement Lakehouse Federation
- Manage cross-platform data access
Topic 2: Monitoring and Alerting10%- Track data lineage and metrics
- Set up alerts and notifications
- Monitor pipeline performance and health
Topic 3: Cost & Performance Optimisation13%- Improve query and pipeline performance
- Optimize compute and storage resources
- Apply cost management best practices
Topic 4: Data Ingestion & Acquisition7%- Ingest data from diverse sources
- Use Auto Loader and structured streaming
- Handle incremental and batch data loads
Topic 5: Data Transformation, Cleansing, and Quality10%- Enforce data quality standards
- Apply data cleansing and validation rules
- Implement schema evolution and management
Topic 6: Debugging and Deploying10%- Implement CI/CD and DevOps practices
- Deploy using Asset Bundles, CLI, and APIs
- Troubleshoot and debug pipelines
Topic 7: Data Modelling6%- Implement dimensional and relational models
- Optimize table design and partitioning
- Design Medallion Architecture
Topic 8: Ensuring Data Security and Compliance10%- Ensure data privacy and compliance
- Implement access control and permissions
- Secure data at rest and in transit
Topic 9: Developing Code for Data Processing using Python and SQL22%- Implement complex data processing logic
- Write efficient and maintainable code
- Use Databricks-specific libraries and APIs
Topic 10: Data Governance7%- Manage data assets and metadata
- Use Unity Catalog for governance
- Enforce data policies and standards
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) Sample Questions:
Question #1

대규모 분산형 Databricks 배치 및 스트리밍 파이프라인의 모든 노드에서 일관된 개인 식별 정보(PII) 마스킹을 보장하는 데 있어 주요 기술적 과제는 무엇입니까?

  • A. 동적 데이터 마스킹은 저장 데이터에만 적용되므로 쿼리 성능에 영향을 미치지 않습니다.
  • B. Databricks의 네이티브 마스킹은 모든 하위 외부 Databricks 시스템과 자동으로 동기화됩니다.
  • C. 개인 식별 정보(PII) 마스킹은 직접 식별자에만 필요합니다.
  • D. 마스킹 기능은 Unity Catalog를 통해 표준화 및 관리되어야 하며, 데이터 불일치를 방지하기 위해 모든 관련 데이터 세트에 걸쳐 적용되어야 합니다.
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Correct Answer: D  🗳️

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Question #2

운영 환경에서는 외부 변경 데이터 캡처(CDC) 피드의 업데이트를 Delta Lake 테이블에 상시 실행되는 구조화된 스트림 작업으로 점진적으로 적용합니다. 이 테이블의 데이터를 처음 마이그레이션했을 때 최적화(OPTIMIZE) 작업이 실행되어 대부분의 데이터 파일 크기가 1GB로 조정되었습니다. 스트리밍 운영 작업에 대해 자동 최적화(Auto Optimize)와 자동 압축(Auto Compaction) 기능이 모두 활성화되어 있었습니다. 최근 데이터 파일을 검토한 결과, 대부분의 데이터 파일 크기가 64MB 미만인 것으로 나타났지만, 테이블의 각 파티션에는 최소 1GB의 데이터가 포함되어 있고 전체 테이블 크기는 10TB를 초과합니다.
다음 중 파일 크기가 작아진 이유를 가장 잘 설명하는 것은 무엇입니까?

  • A. Databricks는 테이블의 전체 데이터 크기를 기반으로 더 작은 목표 파일 크기로 자동 조정했습니다.
  • B. Databricks는 각 파티션의 데이터 양을 기반으로 더 작은 목표 파일 크기로 자동 조정했습니다.
  • C. 테이블에서 계산된 Z 순서 인덱스가 파일 압축을 방해하고 있습니다. C: 테이블에서 계산된 Bloom 필러 인덱스가 파일 압축을 방해하고 있습니다.
  • D. Databricks는 MERGE 작업 시간을 단축하기 위해 대상 파일 크기를 더 작게 자동 조정했습니다.
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Correct Answer: D  🗳️

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Question #3

데이터 엔지니어와 플랫폼 엔지니어가 시스템 작업을 자동화하기 위해 협업하고 있습니다. 특정 Databricks 일일 작업이 성공적으로 완료된 경우에만 Databricks 외부에서 스크립트를 실행해야 합니다. Databricks CLI 명령어를 사용하여 해당 작업의 마지막 실행 시간을 확인했습니다. 이 작업을 위해 필요한 명령어 옵션은 무엇입니까?

  • A. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
  • B. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
  • C. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active-only
  • D. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
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Correct Answer: B  🗳️

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Question #4

스트림 정적 조인과 정적 델타 테이블에 관한 다음 설명 중 올바른 것은 무엇입니까?

  • A. 체크포인트 디렉터리는 조인에 존재하는 고유 키의 상태 정보를 추적하는 데 사용됩니다.
  • B. 스트림 정적 조인은 일관성 문제로 인해 정적 델타 테이블을 사용할 수 없습니다.
  • C. 스트림-정적 조인의 각 마이크로배치는 작업 초기화 시점의 최신 버전의 정적 델타 테이블을 사용합니다.
  • D. 스트림-정적 조인의 각 마이크로배치는 해당 마이크로배치 시점의 정적 델타 테이블의 최신 버전을 사용합니다.
  • E. 체크포인트 디렉터리는 정적 델타 테이블의 업데이트를 추적하는 데 사용됩니다.
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Correct Answer: D  🗳️

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Question #5

다음 코드는 기존 워크로드에서 Databricks 노트북으로 마이그레이션되었습니다.

해당 코드는 성공적으로 실행되어 논리적으로 올바른 결과를 제공하지만, 제어권을 장악합니다.
약 1GB의 데이터를 추출하고 불러오는 데 20분이 소요됩니다.
다음 중 이러한 행동에 대한 가능한 설명은 무엇입니까?

  • A. Databricks 환경에서 Python은 항상 Scala보다 느리게 실행됩니다. run.py 스크립트를 Scala로 리팩토링해야 합니다.
  • B. %sh는 파일 이동 작업을 분산 처리하지 않습니다. 마지막 코드 줄을 %fs를 사용하도록 업데이트해야 합니다.
  • C. 클론하는 대신, `%sh pip install` 명령어를 사용하여 Python 코드가 클러스터의 모든 노드에서 병렬로 실행될 수 있도록 해야 합니다.
  • D. %sh는 드라이버 노드에서 셸 코드를 실행합니다. 이 코드는 워커 노드나 Databricks 최적화 Spark의 이점을 활용하지 않습니다.
  • E. %sh는 Git을 수집 및 설치하기 위해 클러스터 재시작을 트리거합니다. 지연 시간의 대부분은 클러스터 시작 시간과 관련이 있습니다.
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Correct Answer: D  🗳️

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