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Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) Sample Questions:
데이터 팀의 구조화된 스트리밍 작업은 하위 마케팅 대시보드를 업데이트하기 위해 품목 판매량에 대한 집계 데이터를 계산하도록 구성되어 있습니다. 마케팅 팀은 각 품목에 대해 해당 프로모션 코드가 사용된 횟수를 추적하기 위한 새로운 필드를 추가했습니다. 주니어 데이터 엔지니어는 기존 쿼리를 다음과 같이 업데이트할 것을 제안했습니다. 제안된 변경 사항은 굵게 표시되어 있습니다.
제안된 쿼리를 실제 운영 환경에 적용하려면 어떤 단계를 추가로 완료해야 합니까?
- A. REFRESH TABLE delta 실행, /item_agg '
- B. 추가 집계를 고려하여 셔플 파티션을 늘립니다.
- C. 스트리밍 쓰기에서 .option(mergeSchema ' , true ' )를 제거합니다.
- D. 새로운 체크포인트 위치를 지정하세요
Correct Answer: D 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
데이터 엔지니어가 데이터 편향과 빈번하게 변경되는 쿼리 필터 열로 인해 문제가 발생하는 관리형 델타 테이블을 최적화하고 있습니다. 엔지니어는 쿼리 패턴이 변화할 때 발생하는 비용이 많이 드는 데이터 재작성을 피하고자 합니다. 테이블 크기는 1TB 미만입니다.
데이터 엔지니어는 어떻게 이 요구 사항을 충족해야 할까요?
- A. Z-순서를 적용하세요. 기존 파일을 수정하지 않고도 데이터 레이아웃을 유연하게 재구성할 수 있고, 새로운 필터 열에 쉽게 적용할 수 있습니다.
- B. Hive 스타일 파티셔닝을 사용하세요. 효율적인 데이터 건너뛰기 기능을 제공하며 언제든지 파티션 열을 쉽게 변경할 수 있습니다.
- C. 데이터 불균형을 효율적으로 처리하고, 기존 데이터를 덮어쓰지 않고 클러스터링 키를 변경할 수 있으며, 변화하는 쿼리 패턴에 적응할 수 있는 유동적 클러스터링을 활성화합니다.
- D. 쿼리 패턴이 변경될 때 유연성을 극대화하고 유지 관리를 최소화하기 위해 파티셔닝과 Z-순서를 결합합니다.
Correct Answer: C 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
데이터 엔지니어가 Databricks의 Lakeflow Declarative Pipelines(LDP)를 사용하여 고객 데이터를 수집하는 간단한 데이터 파이프라인을 구축하고 있습니다. 원시 고객 데이터는 클라우드 스토리지에 JSON 형식으로 저장되어 있습니다. 이 작업은 원시 JSON 데이터를 읽어 추가 처리를 위해 Delta 테이블에 쓰는 Lakeflow Declarative Pipelines를 생성하는 것입니다.
어떤 코드 조각이 원시 JSON 데이터를 올바르게 가져와 LDP를 사용하여 델타 테이블을 생성합니까?
- A. import dlt
@dlt.table
def raw_customers():
return spark.read.json( " s3://my-bucket/raw-customers/ " ) - B. import dlt
@dlt.table
def raw_customers():
return spark.read.format( " csv " ).load( " s3://my-bucket/raw-customers/ " ) - C. import dlt
@dlt.table
def raw_customers():
return spark.read.format( " parquet " ).load( " s3://my-bucket/raw-customers/ " ) - D. import dlt
@dlt.view
def raw_customers():
return spark.format.json( " s3://my-bucket/raw-customers/ " )
Correct Answer: A 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
Delta Lake 테이블은 사용자 콘텐츠에 대한 메타데이터를 나타내며 다음과 같은 스키마를 가집니다.
위 스키마를 바탕으로 Delta 테이블을 파티셔닝하기에 적합한 열은 무엇일까요? user_id (LONG), post_text (STRING), post_id (STRING), longitude (FLOAT), latitude (FLOAT), post_time (TIMESTAMP), date (DATE) 열이 각각 LONG, STRING, LONG, LONG, LONG, LOT, DATE입니다.
- A. 게시 시간
- B. 게시물 ID
- C. 사용자_id
- D. 날짜
Correct Answer: D 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
아래 쿼리를 사용하여 Delta Lake 테이블이 생성되었습니다.
다음 질문을 고려해 보세요.
DROP TABLE prod.sales_by_store -
워크스페이스 관리자가 이 명령문을 실행하면 어떤 결과가 발생할까요?
- A. 해당 테이블은 카탈로그에서 제거되지만 데이터는 저장소에 남아 있습니다.
- B. 해당 테이블은 카탈로그에서 제거되고 데이터는 삭제됩니다.
- C. Delta Lake에서 운영 데이터 삭제를 방지하므로 오류가 발생합니다.
- D. 데이터는 삭제된 것으로 표시되지만 타임 트래블을 통해 복구할 수 있습니다.
- E. COMMIT 명령이 실행될 때까지 아무 일도 일어나지 않습니다.
Correct Answer: B 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
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