Databricks Databricks-Certified-Professional-Data-Engineer Korean valid dump torrent : Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version)
- Exam Code: Databricks-Certified-Professional-Data-Engineer-KR
- Exam Name: Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version)
- Updated: Nov 12, 2026
- Q&As: 285 Questions and Answers
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Databricks Certified Professional Data Engineer Certification Exam is a rigorous and comprehensive certification exam that validates a professional's competence in Databricks data engineering principles and techniques. With the exponential growth of data management and processing, data professionals need certification to show that they are experts in handling data. With Databricks Certified Professional Data Engineer certification, data engineers can confidently build, implement and maintain effective and scalable data engineering solutions.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
To take the Databricks Certified Professional Data Engineer certification exam, candidates must have a strong background in data engineering and experience working with Databricks. Databricks-Certified-Professional-Data-Engineer-KR exam consists of multiple-choice questions and hands-on tasks that require candidates to demonstrate their ability to design, build, and manage data processing systems using Databricks. Databricks-Certified-Professional-Data-Engineer-KR exam covers a wide range of topics, including data ingestion, data transformation, data storage, data processing, and data visualization. Upon successful completion of the exam, candidates will receive a Databricks Certified Professional Data Engineer certification, which demonstrates their expertise in using Databricks to build and maintain data processing systems.
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Databricks-Certified-Professional-Data-Engineer certification exam is a valuable credential for data engineers who work with Databricks. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification demonstrates the candidate's expertise in Databricks technology and data engineering concepts. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification also demonstrates the candidate's commitment to professional development and continuous learning.
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Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Integration with Databricks APIs and tools - Data transformation and aggregation |
| Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization |
| Monitoring and Alerting | 10% | - Pipeline observability and logging - Performance and health monitoring - Setting up alerts and notifications |
| Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion |
| Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching |
| Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Access control and permissions - Compliance standards implementation |
| Data Modelling | 6% | - Schema design and management - Medallion Architecture implementation - Delta Lake table design |
| Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) Sample Questions:
시설 모니터링 팀이 Delta 테이블의 device_readings 데이터를 기반으로 거의 실시간에 가까운 Power BI 대시보드를 구축하고 있습니다.
* device_id 문자열 - 고유 센서 ID
* event_ts TIMESTAMP - 데이터 수집 타임스탬프(UTC)
* temperature_c DOUBLE - 섭씨 온도
* 참고 사항 STRING
각 센서에 대해, 팀은 2분 간격으로 겹치지 않는 5분 간격(예: 00:02-00:07, 00:07-00:12 등)으로 한 행씩을 생성하여 해당 구간의 평균 온도를 표시해야 합니다.
결과에는 각 구간의 시작 및 종료 타임스탬프가 포함되어야 하위 도구에서 시계열 막대를 정확하게 그릴 수 있습니다. 다음 쿼리 중 이 요구 사항을 충족하는 쿼리는 무엇입니까?
- A. 선택하다
장치 ID,
date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 2 MINUTES AS bucket_start, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 7 MINUTES AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM device_readings GROUP BY device_id, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) ORDER BY device_id, bucket_start; - B. 선택하다
장치 ID,
윈도우 시작을 버킷 시작으로,
윈도우 끝을 버킷 끝으로,
AVG(온도_섭씨) AS avg_temp_5m
device_readings에서
GROUP BY device_id, window(event_ts, ' 5 minutes ' , ' 5 minutes ' , ' 2 minutes ' ) ORDER BY device_id, bucket_start; - C. 선택하다
장치 ID,
이벤트_ts,
AVG(온도_c) OVER (
device_id로 분할
event_ts를 기준으로 정렬
이전 5분 간격과 현재 행 사이의 범위
) AS 평균 온도 5m
device_readings에서
WINDOW w AS (window(event_ts, ' 5 minutes ' , ' 2 minutes ' )); - D. 버킷을 다음과 같이 사용(
선택하다
장치 ID,
window(event_ts, '5분', '5분', '2분') AS win, temperature_c FROM device_readings) SELECT device_id, win.start AS bucket_start, win.end AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM buckets GROUP BY device_id, win ORDER BY device_id, bucket_start;
Correct Answer: D 🗳️
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데이터 엔지니어가 소스에서 가져온 CDC(변경 데이터 캡처) 데이터를 처리하기 위해 Lakeflow 선언적 파이프라인을 구성하고 있습니다. 소스 이벤트는 때때로 순서대로 도착하지 않으며, 동일한 update_timestamp를 갖지만 update_sequence_id가 다른 여러 업데이트가 발생할 수 있습니다.
데이터 엔지니어는 이벤트 순서가 올바르게 유지되도록 하기 위해 무엇을 해야 할까요?
- A. AUTO CDC API에서 track_history_column_list를 [event_timestamp, event_id]로 설정합니다.
- B. O AUTO CDC API에서 SEQUENCE BY STRUCT(event_timestamp, update_sequence_id)를 사용하세요.
- C. LDP 파이프라인에서 update_timestamp와 같은 파티션 내에서 update_sequence_id를 정렬하기 위해 윈도우 함수를 사용합니다.
- D. LDP에서 순서가 잘못되었거나 중복된 레코드를 제거하려면 dropDuplicates()를 사용하십시오.
Correct Answer: B 🗳️
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데이터 엔지니어링 팀이 Databricks Unity Catalog에서 액세스 제어를 구성하고 있습니다. 그들은 analyst_group에 sales catalog에 대한 SELECT 권한을 부여했으며, 이 그룹의 구성원은 카탈로그 내의 모든 현재 및 향후 스키마, 테이블 및 뷰에 대한 SELECT 액세스 권한을 자동으로 갖게 될 것으로 예상합니다.
Unity Catalog에서 권한 상속 동작을 어떻게 설명하나요?
- A. 스키마 수준에서 부여된 권한은 카탈로그 수준의 권한보다 우선하며 명시적으로 취소하지 않는 한 접근을 차단합니다.
- B. 카탈로그에 SELECT 권한을 부여하면 해당 카탈로그 내의 현재 및 향후 모든 스키마, 테이블 및 뷰에 SELECT 권한이 자동으로 적용됩니다.
- C. Unity 카탈로그의 권한은 계층적으로 적용되지 않습니다. 카탈로그 수준에서 권한이 부여되었더라도 각 스키마 및 테이블에 대해 SELECT 권한을 명시적으로 부여해야 합니다.
- D. 카탈로그 수준에서 SELECT 권한을 부여하는 것은 기존 스키마 및 테이블에는 적용되지만 향후 생성될 스키마 및 테이블에는 적용되지 않습니다.
Correct Answer: C 🗳️
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데이터 엔지니어가 프로덕션 수준의 데이터 파이프라인 구축을 위한 도구를 평가하고 있습니다. 이 팀은 클라우드 객체 스토리지에서 변경 데이터를 처리하고, 유효하지 않은 레코드를 필터링하거나 분리하여, 정제된 데이터가 하위 시스템에 신속하게 전달되도록 해야 합니다. 팀 규모는 작고 마감 기한이 촉박한 상황에서, 파이프라인의 감사 및 유지 관리 용이성을 유지하면서 운영 오버헤드를 최소화하고자 합니다.
데이터 엔지니어는 어떤 접근 방식을 구현해야 할까요?
- A. 구조적 스트리밍을 사용하는 자동 로더를 통해 데이터 수집을 구현하고, 체크포인트 및 병합 로직을 사용하여 유효하지 않은 데이터 처리 및 테이블 업데이트를 관리합니다.
- B. LDP를 사용하여 스트리밍 테이블과 구체화된 뷰를 포함하는 선언적 파이프라인을 구축하고, 데이터 기대치 및 증분 처리에 대한 내장 지원을 활용합니다.
- C. 하이브리드 방식을 사용합니다. 자동 로더를 사용하여 브론즈 테이블에 데이터를 수집한 다음, Databricks 워크플로에서 SQL 쿼리를 사용하여 처리하고, 정해진 일정에 따라 정리된 실버 및 골드 테이블을 생성합니다.
- D. Spark 작업을 통해 Delta 테이블에 직접 데이터를 수집하고, UDF를 사용하여 데이터 품질 필터를 적용하고, Lakeflow Declarative Pipelines(LDP)를 사용하여 구체화된 뷰를 생성합니다.
Correct Answer: B 🗳️
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데이터 엔지니어가 Delta 테이블 두 개(주문 테이블과 고객 테이블)를 조인하여 일일 대조 보고서를 작성하고 있습니다. 고객 테이블에는 일부 행에 NULL 값이 포함된 지역 열이 있습니다.
다음 쿼리를 실행한 후 엔지니어는 지역 정보가 NULL인 고객의 주문이 결과 집합에서 누락된 것을 발견했습니다.
o.order_id, c.region을 선택합니다.
주문부터
JOIN 고객 c
ON o.customer_id = c.customer_id
c.region이 'EMEA'인 경우
또는 c.region = NULL;
어떤 조치를 취해야 지역 정보가 NULL인 고객의 주문이 결과에 포함됩니까?
- A. WHERE 절을 WHERE COALESCE(c.region, 'GLOBAL') = 'EMEA'로 바꿉니다.
- B. 동등 결합 조건을 ON o.customer_id <=> c.customer_id로 바꿉니다.
- C. WHERE 절에서 c.region = NULL을 c.region IS NULL로 바꿉니다.
- D. WHERE 절을 WHERE NULLIF(c.region, NULL) = 'EMEA'로 바꾸세요.
Correct Answer: C 🗳️
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