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Databricks Databricks-Certified-Data-Engineer-Professional Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Topic 2: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions |
| Topic 3: Data Sharing and Federation | 5% | - Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing |
| Topic 4: Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Topic 5: Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code |
| Topic 6: Data Modelling | 6% | - Design Medallion Architecture - Optimize table design and partitioning - Implement dimensional and relational models |
| Topic 7: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Topic 8: Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
| Topic 9: Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics |
| Topic 10: Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) Sample Questions:
다음 중 Delta Lake의 최적화된 쓰기를 설명하는 문장은 무엇입니까?
- A. 쓰기 작업이 완료된 후 파일을 추가로 압축할 수 있는지 확인하기 위해 비동기 작업이 실행됩니다. 예, 기본값인 1GB를 목표로 최적화 작업이 실행됩니다.
- B. 최적화된 쓰기 방식은 디렉터리 파티션 대신 논리 파티션을 사용하며, 파티션 경계는 메타데이터에만 표현되므로 더 적은 수의 작은 파일이 기록됩니다.
- C. 쓰기 작업 전에 셔플이 발생하여 데이터를 그룹화함으로써 각 실행기가 디렉터리 파티션에 따라 여러 파일을 쓰는 대신 더 적은 수의 파일을 생성합니다.
- D. 작업 클러스터가 종료되기 전에 가장 최근 작업 중에 수정된 모든 테이블에 대해 OPTIMIZE가 실행됩니다.
Correct Answer: C 🗳️
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데이터 엔지니어링 팀이 Databricks 프로젝트에서 협업하고 있는데, 각 팀원은 변경 사항을 메인 브랜치에 병합하기 전에 코드를 독립적으로 개발하고 테스트해야 합니다.
그들은 의도치 않은 덮어쓰기나 브랜치 전환 문제를 방지하는 동시에 모든 작업이 버전 관리되고 CI/CD 파이프라인에 통합될 수 있도록 보장하고자 합니다.
데이터 엔지니어는 어떻게 협업을 달성해야 할까요?
- A. 각 팀원은 동일한 원격 Git 저장소에 연결된 자신만의 Databricks Git 폴더를 생성하고, 개인 폴더 내의 자체 개발 브랜치에서 작업합니다.
- B. 팀 구성원은 작업 공간의 공유 폴더에서 노트북을 직접 편집하고, 변경 사항을 주기적으로 Git 폴더에 복사하여 버전 관리를 수행합니다.
- C. 모든 팀 구성원은 동일한 Databricks Git 폴더에서 작업하며 해당 공유 폴더에서 직접 Git 작업(pull, push, commit, branch switching)을 수행합니다.
- D. 팀 구성원은 Databricks CLI를 사용하여 Git 저장소를 복제하고 클러스터의 웹 터미널에서 Git 작업을 수행합니다.
Correct Answer: A 🗳️
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팀의 주니어 데이터 엔지니어가 다음 코드 블록을 구현했습니다.
뷰 `new_events`에는 `events Delta` 테이블과 동일한 스키마를 가진 레코드 배치가 포함되어 있습니다. `event_id` 필드는 이 테이블의 고유 키 역할을 합니다.
이 쿼리가 실행될 때, 기존 레코드와 동일한 event_id를 가진 새 레코드는 어떻게 처리됩니까?
- A. 그들은 무시당합니다.
- B. 병합되었습니다.
- C. 삭제되었습니다.
- D. 삽입됩니다.
- E. 업데이트되었습니다.
Correct Answer: A 🗳️
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데이터 엔지니어가 `display(df.collect())`를 통해 결과를 출력하기 전에 여러 변환 작업을 수행하는 대화형 노트북에서 작업하고 있습니다. 이 노트북에는 광범위한 변환과 크로스 조인이 포함되어 있습니다.
데이터 엔지니어가 "Spark 드라이버가 예기치 않게 중지되어 다시 시작됩니다. 노트북이 자동으로 다시 연결됩니다."라는 오류 메시지를 받았습니다. 데이터 엔지니어는 어떤 조치를 취해야 할까요?
- A. 컴퓨팅 메트릭 UI를 확인하여 실행기의 메모리 사용률이 90%를 초과하는지 확인하십시오.
- B. Spark UI에서 각 단계에 할당된 작업 수를 확인하세요. 실행기 수가 줄어들고 있습니다.
- C. 드라이버 오류를 방지하기 위해 노트북을 단일 노드 클러스터에서 실행하십시오.
- D. 드라이버 노드에 메모리 부담을 주지 않도록 코드를 다시 작성하십시오.
Correct Answer: D 🗳️
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분석팀은 데이터 엔지니어링팀이 생성한 고객 거래 델타 테이블(약 200억 건의 레코드)을 대상으로 Databricks SQL에서 단기 실험을 실행하려고 합니다. 데이터 엔지니어링팀은 가동 중지 시간을 최소화하고 진행 중인 ETL 프로세스에 영향을 미치지 않도록 어떤 전략을 사용해야 할까요?
- A. 분석팀을 위해 테이블을 딥 클론합니다.
- B. 분석팀을 위해 테이블을 얕은 복제합니다.
- C. 분석팀에게 프로덕션 테이블에 대한 직접 액세스 권한을 부여합니다.
- D. CTAS 문을 사용하여 분석팀을 위한 새 테이블을 생성합니다.
Correct Answer: B 🗳️
Explanation: Only visible for itPass4sure members. You can sign-up / login (it's free).
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