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Databricks Certified Professional Data Engineer exam consists of multiple-choice questions and is conducted online. Databricks-Certified-Professional-Data-Engineer-KR exam is intended to measure the candidate's proficiency in various areas, such as Spark architecture, Spark programming, data processing, data analysis, and data modeling. Databricks-Certified-Professional-Data-Engineer-KR exam also tests the candidate's ability to optimize Spark performance and troubleshoot Spark applications. It is recommended that individuals who plan to take Databricks-Certified-Professional-Data-Engineer-KR exam have at least two years of hands-on experience in big data technologies and Apache Spark.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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The Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification verifies that the candidate has significant experience in implementing big data solutions that operate on the Databricks Delta Architecture. To earn the certification, a candidate must pass a 90-minute exam consisting of up to 50 multiple-choice and multiple-select questions. The Databricks-Certified-Professional-Data-Engineer certification is valid for two years from the date of passing the exam.
Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Debugging and Deploying | 10% | - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors |
| Data Governance | 7% | - Policy enforcement - Data lineage and metadata tracking - Unity Catalog management |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Data transformation and aggregation - Batch and incremental processing logic |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
| Data Modelling | 6% | - Delta Lake table design - Schema design and management - Medallion Architecture implementation |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) Sample Questions:
데이터 거버넌스 팀은 개인 식별 정보(PH)가 포함된 모든 테이블에 명확한 주석을 추가해야 한다는 요구 사항을 도입했습니다. 여기에는 열 주석, 테이블 주석 추가 및 사용자 지정 테이블 속성 "contains_pii" = true 설정이 포함됩니다.
다음 SQL DDL 문이 실행되어 새 테이블이 생성됩니다.
이 세 가지 요구 사항이 충족되었는지 수동으로 확인할 수 있는 명령은 무엇입니까?
- A. dev.pii 테스트 상세 설명
- B. 확장된 dev.pii 테스트에 대해 설명하세요
- C. 개발 테이블 표시
- D. 개발 이력 설명 dev.pii 테스트
- E. dev.pii 테스트의 TBL 속성 표시
Correct Answer: B 🗳️
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Databricks SQL 대시보드가 다음 쿼리 패턴을 사용하여 Delta Lake 테이블 모음에 있는 총 레코드 수를 모니터링하도록 구성되었습니다.
테이블에서 COUNT(*)를 선택합니다 -
다음 중 대시보드가 업데이트될 때마다 결과가 생성되는 방식을 설명하는 것은 무엇입니까?
- A. 전체 행 수는 모든 데이터 파일을 스캔하여 계산됩니다.
- B. 총 레코드 수는 Delta 거래 로그에서 계산됩니다.
- C. 전체 레코드 수는 Hive 메타스토어에서 계산됩니다.
- D. 전체 레코드 수는 파케트 파일 메타데이터를 기반으로 계산됩니다.
- E. 새로 고침(REFRESH)을 실행하지 않으면 캐시된 결과에서 총 행 수가 반환됩니다.
Correct Answer: B 🗳️
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Databricks CLI는 job_id 매개변수를 전달하여 기존 작업을 실행하는 데 사용됩니다. 작업 실행 요청이 성공적으로 제출되었음을 나타내는 응답에는 run_id 필드가 포함됩니다. 이 필드 옆에 있는 숫자가 무엇을 나타내는지 설명하는 문장은 무엇입니까?
- A. 이 필드에 job_id가 반환됩니다.
- B. 이 워크스페이스에서 작업 정의가 실행된 횟수입니다.
- C. 새로 시작된 실행의 전역적으로 고유한 ID입니다.
- D. 작업 ID와 작업 실행 횟수를 연결하여 반환합니다.
Correct Answer: C 🗳️
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Delta Lake 테이블은 사용자 콘텐츠 게시물에 대한 메타데이터를 나타내며 다음과 같은 스키마를 가집니다.
* 사용자 ID LONG
* post_text 문자열
* 게시물 ID 문자열
* 경도 부동소수점
* 위도 부동소수점
* post_time 타임스탬프
* 날짜 날짜
위 스키마를 기준으로 볼 때, 델타 테이블을 파티셔닝하기에 적합한 열은 무엇입니까?
- A. 사용자 ID
- B. 게시물 ID
- C. 날짜
- D. 게시 시간
Correct Answer: C 🗳️
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
데이터 엔지니어가 S3 버킷에 저장된 환자 진료 기록 데이터를 일괄 처리하는 시스템을 설계하고 있습니다. 이 시스템은 encounter_id, patient_id, encounter_date, diagnosis_code, treatment_cost 열을 가진 Delta 테이블(patient_encounters)을 생성합니다. 이 테이블은 patient_id와 encounter_date를 기준으로 자주 조회되므로 빠른 처리 속도가 요구됩니다. 또한, 세분화된 접근 제어가 필수적입니다. 엔지니어는 유지 관리를 최소화하고 성능을 향상시키고자 합니다.
데이터 엔지니어는 patient_encounters 테이블을 어떻게 생성해야 할까요?
- A. Hive Metastore에 관리형 테이블을 생성합니다. 접근 제어를 위해 Hive Metastore 권한을 구성하고, 예측 최적화를 활용하여 쿼리 성능을 향상시키고 유지 관리를 간소화합니다.
- B. Unity Catalog에 관리형 테이블을 생성합니다. 접근 제어를 위해 Unity Catalog 권한을 구성하고, 예측 최적화를 활용하여 쿼리 성능을 향상시키고 유지 관리를 간소화합니다.
- C. Unity Catalog에 관리형 테이블을 생성합니다. 접근 제어를 위해 Unity Catalog 권한을 구성하고, 최적의 성능을 위해 OPTIMIZE 및 VACUUM 명령이 매일 실행되도록 작업을 예약합니다.
- D. Unity Catalog에 외부 테이블을 생성하고 데이터 파일의 S3 위치를 지정합니다. 테이블 속성을 통해 예측 최적화를 활성화하고 접근 제어를 위해 Unity Catalog 권한을 구성합니다.
Correct Answer: B 🗳️
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