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Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) : Databricks-Certified-Data-Engineer-Professional Korean Exam
- 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 16, 2026
- Q & A: 250 Questions and Answers
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Databricks Databricks-Certified-Data-Engineer-Professional Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Improve query and pipeline performance - Apply cost management best practices |
| Topic 2: Data Sharing and Federation | 5% | - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing - Manage cross-platform data access |
| Topic 3: Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic |
| Topic 4: Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices |
| Topic 5: Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads |
| Topic 6: Data Governance | 7% | - Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata |
| Topic 7: Data Modelling | 6% | - Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture |
| Topic 8: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions |
| Topic 9: Monitoring and Alerting | 10% | - Set up alerts and notifications - Track data lineage and metrics - Monitor pipeline performance and health |
| Topic 10: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) Sample Questions:
데이터 엔지니어링 팀의 주니어 멤버가 Databricks 노트북의 언어 상호 운용성을 탐색하고 있습니다. 아래 코드의 목표는 geo_lookup 테이블에 있는 아프리카 대륙 국가에서 발생한 모든 판매 내역을 표시하는 것입니다.
코드를 실행하기 전에 현재 데이터베이스에서 SHOW TABLES 명령을 실행하면 데이터베이스에 geo_lookup과 sales라는 두 개의 테이블만 있는 것으로 나타납니다.
대화형 노트북에서 이러한 명령 셀을 순서대로 실행했을 때의 결과를 올바르게 설명하는 문장은 무엇입니까?
- A. 두 명령 모두 실패합니다. 새로운 변수, 테이블 또는 뷰가 생성되지 않습니다.
- B. 첫 번째 명령은 성공하고 두 번째 명령은 실패합니다. countries at은 PySpark DataFrame을 나타내는 Python 변수입니다.
- C. 1번 명령은 성공합니다. 2번 명령은 접근 가능한 모든 데이터베이스에서 countries af라는 이름의 테이블 또는 뷰를 검색합니다. 해당 엔티티가 존재하면 2번 명령도 성공합니다.
- D. 첫 번째 명령은 성공하고 두 번째 명령은 실패합니다. countries at은 문자열 목록을 포함하는 Python 변수가 됩니다.
- E. 두 명령 모두 성공할 것입니다. show tables 명령을 실행하면 countries at과 sales at이 뷰로 등록된 것을 확인할 수 있습니다.
Correct Answer: D 🗳️
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신입 데이터 엔지니어가 Databricks Jobs UI를 사용하여 일련의 작업을 수동으로 구성했습니다.
엔지니어는 자신의 업무를 검토하던 중, 각 작업의 "소유자"로 자신이 등재되어 있음을 알게 됩니다.
그들은 "소유자" 권한을 "DevOps" 그룹으로 이전하려고 시도하지만, 이 작업을 성공적으로 완료하지 못합니다.
다음 중 어떤 설명이 이러한 권한 이전을 막는 요인을 나타냅니까?
- A. Databricks 작업에는 소유자가 한 명만 있어야 합니다. "소유자" 권한은 그룹에 할당할 수 없습니다.
- B. 기본 "admins" 그룹 외에는 개별 사용자에게만 작업에 대한 권한을 부여할 수 있습니다.
- C. Databricks 작업 생성자는 항상 "소유자" 권한을 가지며, 이 설정은 변경할 수 없습니다.
- D. 사용자는 자신이 해당 그룹의 구성원인 경우에만 작업 소유권을 그룹으로 이전할 수 있습니다.
- E. 워크스페이스 관리자만 그룹에 "소유자" 권한을 부여할 수 있습니다.
Correct Answer: A 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
대규모 분산형 Databricks 배치 및 스트리밍 파이프라인의 모든 노드에서 일관된 개인 식별 정보(PII) 마스킹을 보장하는 데 있어 주요 기술적 과제는 무엇입니까?
- A. Databricks의 네이티브 마스킹은 모든 하위 외부 Databricks 시스템과 자동으로 동기화됩니다.
- B. 동적 데이터 마스킹은 저장 데이터에만 적용되므로 쿼리 성능에 영향을 미치지 않습니다.
- C. 마스킹 기능은 Unity Catalog를 통해 표준화 및 관리되어야 하며, 데이터 불일치를 방지하기 위해 모든 관련 데이터 세트에 걸쳐 적용되어야 합니다.
- D. 개인 식별 정보(PII) 마스킹은 직접 식별자에만 필요합니다.
Correct Answer: C 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
데이터 엔지니어링 팀은 Databricks Lakehouse Monitoring을 사용하여 Delta 테이블의 핵심 열에 대한 percent_null 지표를 추적합니다.
프로필 메트릭 테이블(prod_catalog.prod_schema.customer_data_profile_metrics)에는 시간별 percent_null 값이 저장됩니다.
팀의 목표는 다음과 같습니다.
percent_null의 일일 평균이 5%를 초과할 때 알림을 발생시키세요.
3일 연속.
문제가 지속되는 동안 알림이 스팸처럼 전송되지 않도록 하십시오.
- A. SELECT AVG(percent_null) AS daily_avg
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY
경고 조건: daily_avg > 5
알림 빈도: 알림이 평가될 때마다 - B. SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY 알림 조건: violation_days >= 3 알림 빈도: 1회만
- C. 일일 평균을 다음과 같이 (
SELECT DATE_TRUNC('DAY', window.end) AS day,
AVG(percent_null) AS avg_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
GROUP BY DATE_TRUNC('DAY', window.end)
)
일, 평균값이 null인 경우 선택
일일 평균에서
날짜순으로 내림차순 정렬
제한 3
경고 조건: 최근 3개 행의 모든 avg_null 값이 5보다 큰 경우
알림 빈도: 한 번만 - D. SELECT percent_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
경고 조건: percent_null > 5
알림 빈도: 최대 24시간마다
Correct Answer: C 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
플랫폼 팀에서 CI/CD를 지원하기 위해 Databricks Asset Bundles의 표준 템플릿을 만들고 있습니다. 이 템플릿은 아티팩트, 워크스페이스 루트 경로 및 실행 ID에 대한 기본값을 지정해야 하며, "dev" 대상을 기본값으로 설정하고 특정 경로를 재정의할 수 있도록 허용해야 합니다. 이러한 요구 사항을 충족하기 위해 팀은 databricks.yml 파일을 어떻게 활용해야 할까요?
- A. 루트, 모듈, 프로필, 액터 및 타겟을 사용합니다. 프로필에는 작업 공간 및 아티팩트 기본값이 포함되고 액터는 실행 ID를 설정합니다.
- B. 프로젝트, 패키지, 환경, ID 및 스테이지를 사용합니다. 환경 설정에서 dev를 기본 스테이지로 설정하고 작업 공간을 재정의합니다.
- C. 최상위 수준에서 bundle, artifacts, workspace, run_as 및 targets를 사용하고, default:true로 설정된 타겟 하나를 지정하고 해당 타겟 아래에서 워크스페이스 경로 또는 아티팩트를 재정의합니다.
- D. 배포, 빌드, 컨텍스트, ID 및 환경을 사용합니다. 개발 환경을 기본 환경으로 설정하고 빌드 아래의 경로를 재정의합니다.
Correct Answer: C 🗳️
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