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

"Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version)", also known as Databricks-Certified-Professional-Data-Engineer exam, is a Databricks Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 285 Q&As to your Databricks-Certified-Professional-Data-Engineer Exam preparation. In the Databricks-Certified-Professional-Data-Engineer exam resources, you will cover every field and category in Databricks Certification Certification helping to ready you for your successful Databricks Certification.

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  • 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
  • Updated: Nov 13, 2026
  • No. of Questions: 285 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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

TopicDetails
Topic 1
  • Monitoring & Logging: This topic includes understanding the Spark UI, inspecting event timelines and metrics, drawing conclusions from various UIs, designing systems to control cost and latency SLAs for production streaming jobs, and deploying and monitoring both streaming and batch jobs.
Topic 2
  • Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.
Topic 3
  • Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.

Reference: https://www.databricks.com/learn/certification/data-engineer-professional

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

Databricks 작업이 3개의 태스크로 구성되었으며, 각 태스크는 Databricks 노트북입니다. 태스크 A는 다른 태스크에 종속되지 않습니다. 태스크 B와 C는 병렬로 실행되며, 각각 태스크 A에 직렬 종속성을 갖습니다.
작업 A와 B는 성공적으로 완료되었지만 작업 C는 예정된 실행 중에 실패했을 경우, 결과적인 상태를 설명하는 문장은 무엇입니까?

  • A. 모든 작업이 성공적으로 완료되지 않으면 레이크하우스에 변경 사항이 커밋되지 않습니다. 작업 C가 실패했으므로 모든 커밋이 자동으로 롤백됩니다.
  • B. 작업 A와 B와 관련된 노트북에 표현된 모든 논리가 성공적으로 완료되었습니다. 작업 C의 일부 작업만 성공적으로 완료되었을 수 있습니다.
  • C. 작업 A와 관련된 노트북에 표현된 모든 로직이 성공적으로 완료되었습니다. 단계 실패로 인해 작업 B와 C는 변경 사항을 커밋하지 않습니다.
  • D. 모든 작업은 종속성 그래프로 관리되므로 모든 작업이 성공적으로 완료될 때까지 Lakehouse에 변경 사항이 커밋되지 않습니다.
  • E. 작업 A와 B와 관련된 노트북에 표현된 모든 로직이 성공적으로 완료되었습니다. 작업 C에서 변경된 사항은 작업 실패로 인해 되돌려집니다.
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Correct Answer: B  🗳️

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상위 시스템에서 변경 데이터 캡처(CDC) 로그가 생성되어 클라우드 객체 스토리지 디렉터리에 기록됩니다. 로그의 각 레코드는 변경 유형(삽입, 업데이트 또는 삭제)과 변경 후 각 필드의 값을 나타냅니다. 소스 테이블에는 pk_id 필드로 식별되는 기본 키가 있습니다.
감사 목적으로 데이터 거버넌스 팀은 소스 시스템에서 유효했던 모든 값에 대한 전체 기록을 유지하고자 합니다. 분석 목적으로는 각 레코드의 가장 최근 값만 기록하면 됩니다. 이러한 레코드를 수집하는 Databricks 작업은 한 시간에 한 번 실행되지만, 각 레코드는 한 시간 동안 여러 번 변경될 수 있습니다.
어떤 솔루션이 이러한 요구 사항을 충족합니까?

  • A. 모든 로그 정보를 브론즈 테이블에 수집하고, merge into를 사용하여 각 pk_id에 대한 가장 최근 항목을 실버 테이블에 삽입, 업데이트 또는 삭제하여 현재 테이블 상태를 복원합니다.
  • B. 각 pk_id에 대해 별도의 히스토리 테이블을 생성하고, 모든 히스토리 테이블을 합치고 가장 최근 상태를 필터링하여 테이블의 현재 상태를 확인합니다.
  • C. Delta Lake의 변경 데이터 피드를 사용하여 외부 시스템의 CDC 데이터를 자동으로 처리하고 모든 변경 사항을 Lakehouse의 모든 종속 테이블에 전파합니다.
  • D. merge into를 사용하여 각 pk_id에 대한 가장 최근 항목을 bronze 테이블에 삽입, 업데이트 또는 삭제한 다음 모든 변경 사항을 시스템 전체에 전파합니다.
  • E. 테이블에 대한 일련의 변경 사항을 순서대로 반복하여 각 변경 사항을 순차적으로 적용합니다. Delta Lake의 버전 관리 기능을 활용하여 감사 로그를 생성합니다.
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Correct Answer: A  🗳️

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데이터 엔지니어가 확정적이고 재현 가능한 릴리스를 보장하기 위해 Databricks Git 폴더를 특정 커밋 SHA에 배포해야 하는 CI/CD 파이프라인을 구축하고 있습니다.
엔지니어는 파이프라인 스크립트에서 Databricks CLI 명령어를 실행합니다.
Databricks 저장소 업데이트 123456789 --main 브랜치
123456789는 REPO_ID입니다.
배포팀은 나중에 파이프라인 스크립트에 아무런 변경 사항이 없었음에도 불구하고 두 번의 연속적인 파이프라인 실행에서 서로 다른 노트북 코드가 생성되었음을 발견했습니다.
이 상황에서 이 명령을 실행하면 어떤 결과가 나오나요?

  • A. Git 폴더는 실행 시마다 main의 HEAD 커밋으로 업데이트되므로, 실행 사이에 main에 새로운 커밋이 푸시되면 다른 코드가 배포됩니다.
  • B. Git 폴더는 첫 실행 시 main이 확인한 커밋 SHA에 고정되어 있으므로, 이후 실행 시에는 main에 새 커밋이 푸시되더라도 항상 동일한 코드가 배포됩니다.
  • C. 이 명령은 Git 폴더의 추적 브랜치 메타데이터만 업데이트합니다. 별도의 Databricks 저장소 풀 명령을 실행하기 전까지는 원격 저장소에서 파일을 가져오지 않습니다.
  • D. --branch는 Databricks 저장소 업데이트에 유효한 플래그가 아니므로 실행 중에 명령이 실패합니다. --tag 및 --sparse-checkout-patterns만 허용됩니다.
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Correct Answer: A  🗳️

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필요한 모듈을 찾을 때 검색할 디렉터리 목록을 담고 있는 파이썬 변수는 무엇입니까?

  • A. os-path
  • B. importlib.resource 경로
  • C. pypi.path
  • D. pylib.source
  • E. ,sys.path
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

신입 데이터 엔지니어에게 DataFrame df를 사용하여 그룹화된 집계를 수행하는 스트리밍 데이터 파이프라인을 개발하라는 요청이 있었습니다. 이 파이프라인은 겹치지 않는 5분 간격마다 평균 습도와 평균 온도를 계산해야 합니다. 이벤트는 장치별로 1분에 한 번씩 기록됩니다.
스트리밍 데이터프레임 df는 다음과 같은 스키마를 가지고 있습니다.
" device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT "
코드 블록:

이 과제를 완료하려면 코드 블록 안의 빈칸을 올바르게 채우는 답을 선택하세요.

  • A. window( " event_time " , " 10 minutes " ).alias( " time " )
  • B. lag( " event_time " , " 10 minutes " ).alias( " time " )
  • C. window( " event_time " , " 5 minutes " ).alias( " time " )
  • D. " event_time "
  • E. to_interval( " event_time " , " 5 minutes " ).alias( " time " )
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Correct Answer: C  🗳️

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