Databricks Databricks-Certified-Data-Engineer-Professional Korean : Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version)
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:
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads |
| Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit |
| Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
| Data Transformation, Cleansing, and Quality | 10% | - Implement schema evolution and management - Apply data cleansing and validation rules - Enforce data quality standards |
| Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs |
| Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
| Data Sharing and Federation | 5% | - Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing |
| Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics |
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) Sample Questions:
데이터 엔지니어가 분석 팀에서 사용할 트랜잭션 델타 테이블을 Databricks에 생성했습니다. 분석 팀은 해당 테이블을 Apache Iceberg 형식을 요구하는 다른 도구와 함께 사용하려고 합니다. 데이터 엔지니어는 어떻게 해야 할까요?
- A. 분석팀에서 사용할 수 있도록 거래 델타 테이블의 아이스버그 복사본을 생성합니다.
- B. 분석팀이 델타 테이블을 지원하는 도구를 사용하도록 요구합니다.
- C. 트랜잭션 델타 테이블을 아이스버그 형식으로 변환하고 균일화를 활성화하여 테이블을 델타 테이블로 읽을 수 있도록 합니다.
- D. 거래 테이블에서 'iceberg' 형식을 균일하게 적용하여 테이블을 Iceberg 테이블로 읽을 수 있도록 합니다.
Correct Answer: C 🗳️
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데이터 거버넌스 팀은 GDPR 준수를 위해 레코드 삭제에 사용되는 코드를 검토하고 있습니다. 그들은 Delta Lake의 users라는 테이블에서 레코드를 삭제하는 데 다음과 같은 로직이 사용된다는 것을 확인했습니다.
user_id가 고유 식별 키이고 delete_requests에 삭제를 요청한 모든 사용자가 포함되어 있다고 가정할 때, 위의 로직을 성공적으로 실행하면 삭제될 레코드에 더 이상 접근할 수 없다는 것이 보장되는지 여부를 설명하는 문장은 무엇이며, 그 이유는 무엇입니까?
- A. 아니요. 델타 캐시는 클러스터가 재시작될 때까지 테이블의 이전 버전에서 가져온 레코드를 반환할 수 있습니다.
- B. 예; 델타 캐시는 디스크에 기록된 최신 데이터 파일을 반영하여 즉시 업데이트됩니다.
- C. 아니요. Delta Lake의 삭제 명령은 병합 명령과 함께 사용할 때만 ACID 보장을 제공합니다.
- D. 예; Delta Lake의 ACID 보장은 삭제 명령이 완전히 성공적으로 완료되어 해당 레코드가 영구적으로 삭제되었음을 보장합니다.
- E. 아니요; 삭제된 레코드가 포함된 파일은 vacuum 명령을 사용하여 무효화된 데이터 파일을 제거하기 전까지는 타임 트래블을 통해 계속 접근할 수 있습니다.
Correct Answer: E 🗳️
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Databricks CLI는 job_id 매개변수를 전달하여 기존 작업을 실행하는 데 사용됩니다. 작업 실행 요청이 성공적으로 제출되었다는 응답에는 run_id 필드가 포함됩니다.
다음 중 이 항목 옆의 숫자가 나타내는 바를 설명하는 문장은 무엇입니까?
- A. 작업 ID와 해당 작업이 수행된 횟수를 연결하여 반환합니다.
- B. 워크스페이스에서 작업 정의가 실행된 횟수입니다.
- C. 워크스페이스에서 실행된 총 작업 수입니다.
- D. 이 필드에는 job_id가 반환됩니다.
- E. 새로 시작된 실행의 전역적으로 고유한 ID입니다.
Correct Answer: E 🗳️
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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 뷰를 정의하고, DLT에서 report 테이블에 대한 기대치에 이 뷰를 참조합니다.
- B. validation_copy, report, report 테이블을 대상으로 왼쪽 외부 조인을 수행하고, 그 결과를 DLT 기대값과 비교하여 report 테이블을 검증하는 함수를 정의하십시오.
- C. 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.
- D. validation_copy 테이블과 report 테이블을 왼쪽 외부 조인하는 임시 테이블을 정의하고, report 테이블의 키 값 중 null이 없어야 한다는 조건을 정의합니다.
Correct Answer: A 🗳️
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데이터 엔지니어링 팀은 세 가지 데이터 소스를 사용하는 시간이 오래 걸리는 데이터 수집 작업을 수행하고 있습니다. 각 노트북은 새 데이터를 로드하는 데 약 1시간이 소요됩니다. 어느 날, 노트북 업데이트로 인해 새로운 필수 구성 매개변수가 추가되면서 작업이 실패했습니다. 팀은 이 문제를 신속하게 해결하고 실패한 소스에서 최신 데이터를 로드해야 합니다. 팀은 어떤 조치를 취해야 할까요?
- A. 누락된 작업 매개변수를 추가하여 작업을 업데이트하고 수동으로 작업을 실행합니다.
- B. 새 매개변수를 사용하여 실행을 수정합니다.
- C. 문제가 발생한 노트북 소유자에게 분석 결과를 공유하여 신속하게 문제를 해결할 수 있도록 합니다.
- D. 새 매개변수를 사용하여 실행을 복구하고, 누락된 작업 매개변수를 추가하여 작업을 업데이트합니다.
Correct Answer: D 🗳️
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