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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
Real Exam Qty:60-70
Exam Format:Scenario-based questions, Multiple select, Multiple choice
Certificate Validity Period:2 years
Exam Price:USD 200
Related Certifications:Databricks Certified Data Engineer Associate
Exam Duration:120 minutes
Passing Score:70%
Available Languages:English
Recommended Training:Databricks Academy
Exam Registration:Databricks Certification Portal
Sample Questions: DOWNLOAD DEMO
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- Automate ETL pipelines and scheduling
- Build and manage workflows using Databricks Jobs
- Pipeline reliability and fault tolerance
Topic 2: Data Modeling and Storage- Schema evolution and data partitioning strategies
- Delta Lake table design and optimization
- Design scalable data lakehouse architectures
Topic 3: Data Ingestion and Transformation- Ingest data using Apache Spark and Databricks
- Transform and clean datasets using Spark SQL and DataFrame APIs
- Handle batch and streaming data pipelines
Topic 4: Security, Governance, Monitoring, and Optimization- Implement Unity Catalog governance and access control
- Cost optimization and performance tuning
- Monitor and optimize Spark workloads

Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam FAQs

The Databricks-Certified-Professional-Data-Engineer Korean exam is a core requirement for the Databricks Certified Professional Data Engineer certification at the Professional level. Earning this validates your skills for credentials like Databricks Certified Data Engineer Associate.

Candidates will face 60-70 questions and have 120 minutes to complete the exam. This means you need strict time management. We strongly recommend taking timed practice tests to ensure you can maintain a steady pace without rushing when under actual exam time pressure.

The official examination fee is USD 200, and you must achieve a score of 70% to pass. Since retaking the test requires paying the full fee again, utilizing accurate practice materials for thorough self-assessment beforehand is crucial to protect your investment.

Candidate requirements include: Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform.. Please verify all eligibility details on the official certification page before scheduling your appointment.

Registration is handled through official testing partners. The exam is delivered via Online proctored exam. You can sign up here:

The vendor suggests following official learning paths to build a foundational understanding:

Once you complete your training, use the 217 practice questions from SureTorrent to refine your exam readiness.

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The syllabus is divided into 4 main domains. Key areas include "Data Modeling and Storage", "Data Ingestion and Transformation", "Production Pipelines and Orchestration". For a comprehensive breakdown of all measured skills, please review the complete exam outline table provided above.

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

Question #1

다음 표는 전자상거래 웹사이트에서 사용자가 장바구니에 담는 상품들을 보여줍니다.

다음 MERGE 문은 스키마 평가가 활성화된 이 테이블을 업데이트 뷰를 사용하여 업데이트하는 데 사용됩니다.

다음 업데이트는 어떻게 처리해야 할까요?

  • A. 대상 스키마에 필요한 열이 누락되었기 때문에 업데이트 내용이 별도의 ' ' 복원 ' ' 열로 이동되었습니다.
  • B. 새롭게 복원된 필드가 대상 스키마에 추가되고, 일치하지 않는 기존 레코드의 경우 동적으로 NULL로 읽힙니다.
  • C. 새 중첩 필드가 대상 스키마에 추가되고, 기존 레코드의 기본 파일이 업데이트되어 새 필드에 대한 NULL 값이 포함됩니다.
  • D. 대상 스키마의 기존 열에 대한 변경이 지원되지 않으므로 업데이트에서 오류가 발생합니다.
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Correct Answer: C  🗳️

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Question #2

시간 단위 배치 작업이 구성되어 클라우드 객체 스토리지 컨테이너에서 데이터 파일을 수집합니다. 각 배치는 해당 시간 동안 소스 시스템에서 생성된 모든 레코드를 나타냅니다. 이러한 레코드를 레이크하우스에 처리하는 배치 작업은 지연된 데이터가 누락되지 않도록 충분한 시간 간격을 두고 실행됩니다. user_id 필드는 데이터의 고유 키이며, 다음과 같은 스키마를 갖습니다.
user_id는 BIGINT, username은 STRING, user_utc는 STRING, user_region은 STRING, last_login은 BIGINT, auto_pay는 BOOLEAN, last_updated는 BIGINT입니다. 모든 새 레코드는 account_history라는 테이블에 입력되며, 이 테이블은 소스와 동일한 스키마에 모든 데이터의 전체 기록을 유지합니다. 시스템의 다음 테이블은 account_current이며, 각 고유 user_id에 대한 최신 값을 나타내는 Type 1 테이블로 구현됩니다.
수백만 개의 사용자 계정과 시간당 수만 건의 레코드가 처리된다고 가정할 때, 매시간 배치 작업의 일부로 설명된 account_current 테이블을 효율적으로 업데이트하는 데 사용할 수 있는 구현 방법은 무엇입니까?

  • A. 계정 내역에서 마지막 업데이트 필드와 최근 처리 시간, 그리고 사용자 ID별 최대 마지막 로그인 시간을 기준으로 레코드를 필터링하고, 각 사용자 ID에 대해 가장 최근 값을 업데이트하거나 삽입하는 병합 문을 작성합니다.
  • B. 사용자 ID별로 그룹화하고 마지막 업데이트 값의 최댓값을 기준으로 필터링하여 계정 내역 테이블에 대한 쿼리 결과를 사용하여 각 배치마다 계정 현재 테이블을 덮어씁니다.
  • C. 자동 로더를 사용하여 계정 기록 디렉터리의 새 파일을 구독하고, 구조화된 스트리밍 트리거를 한 번 실행하여 새로 감지된 파일을 계정 현재 테이블에 일괄 업데이트하도록 구성합니다.
  • D. 계정 내역에서 마지막 업데이트 필드와 최근 처리 시간을 기준으로 레코드를 필터링하고, 사용자 이름이 중복되지 않도록 합니다. 각 사용자 이름에 대해 최신 값을 업데이트하거나 삽입하는 병합 문을 작성하세요.
  • E. Delta Lake 버전 기록을 사용하여 계정 기록의 최신 버전과 이전 버전 간의 차이점을 가져온 다음 이러한 기록을 계정 현재 상태에 기록합니다.
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Correct Answer: A  🗳️

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Question #3

Databricks는 사용자 지정 Python 코드 패키지 설치를 위해 어떤 배포판을 지원합니까?

  • A. sbt
  • B. 크랜
  • C. 크램
  • D. 이름
  • E. 바퀴
  • F. 병
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Correct Answer: D  🗳️

Question #4

데이터 엔지니어는 Databricks의 Delta 테이블이 삭제된 파일을 지속적으로 보존하도록 하는 임무를 맡았습니다.
조직의 데이터 보존 정책을 영구적으로 준수하기 위해 기본값인 7일 대신 15일 동안 데이터를 보관합니다.
삭제된 파일의 보존 기간을 올바르게 설정하는 코드 조각은 무엇입니까?

  • A. spark.conf.set( " spark.databricks.delta.deletedFileRetentionDuration " , " 15 days " )
  • B. spark.sql( " VACUUM my_table RETAIN 15 HOURS " )
  • C. spark.sql( " ALTER TABLE my_table SET TBLPROPERTIES ( ' delta.deletedFileRetentionDuration '
    = ' interval 15 days ' ) " )
  • D. from delta.tables import *
    deltaTable = DeltaTable.forPath(spark, " /mnt/data/my_table " )
    deltaTable.deletedFileRetentionDuration = " interval 15 days "
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Correct Answer: C  🗳️

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Question #5

플랫폼 팀에서 CI/CD를 지원하기 위해 Databricks Asset Bundles용 표준 템플릿을 개발 중입니다. 이 템플릿은 아티팩트, 워크스페이스 루트 경로 및 실행 ID에 대한 기본값을 지정해야 하며, "dev" 대상을 기본값으로 설정하고 특정 경로를 재정의할 수 있도록 허용해야 합니다.
팀은 이러한 요구 사항을 충족하기 위해 databricks.yml 파일을 어떻게 사용해야 할까요?

  • A. 프로젝트, 패키지, 환경, ID 및 스테이지를 사용합니다. 환경 설정에서 dev를 기본 스테이지로 설정하고 작업 공간을 재정의합니다.
  • B. 최상위 수준에서 bundle, artifacts, workspace, run_as 및 targets를 사용하고, default: true로 설정된 타겟 하나를 지정하고 해당 타겟 아래에서 워크스페이스 경로 또는 아티팩트를 재정의합니다.
  • C. 배포, 빌드, 컨텍스트, ID 및 환경을 사용합니다. 개발 환경을 기본 환경으로 설정하고 빌드 아래의 경로를 재정의합니다.
  • D. 루트, 모듈, 프로필, 액터 및 타겟을 사용합니다. 프로필에는 작업 공간 및 아티팩트 기본값이 포함되고 액터는 실행 ID를 설정합니다.
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Correct Answer: B  🗳️

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