Databricks Databricks-Certified-Professional-Data-Engineer Korean : Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version)
- Exam Code: Databricks-Certified-Professional-Data-Engineer Korean
- Exam Name: Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version)
- Updated: Oct 13, 2026
- Q & A: 217 Questions and Answers
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Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
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Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) Sample Questions:
Delta Lake 테이블은 사용자 콘텐츠 게시물에 대한 메타데이터를 나타내며 다음과 같은 스키마를 가집니다.
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE 이 테이블은 날짜 열을 기준으로 파티션되어 있습니다. 다음 필터를 사용하여 쿼리를 실행합니다.
경도 < 20 및 경도 > -20
데이터 필터링 방법을 설명하는 문장은 무엇입니까?
- A. 델타 로그의 통계는 필터링된 범위에 포함된 레코드가 있을 수 있는 데이터 파일을 식별하는 데 사용됩니다.
- B. Delta Engine은 트랜잭션 로그의 행 수준 통계를 사용하여 필터 기준을 충족하는 플라이를 식별합니다.
- C. 최적화 프로그램이 파티션 열과 경도 간의 관계를 알지 못하므로 파일 건너뛰기가 발생하지 않습니다.
- D. Delta Engine은 파케트 파일의 바닥글을 스캔하여 필터 기준을 충족하는 각 행을 식별합니다.
- E. 델타 로그의 통계는 필터링된 범위에 파일이 포함될 수 있는 파티션을 식별하는 데 사용됩니다.
Correct Answer: A 🗳️
Explanation: Only visible for Free4Torrent members. You can sign-up / login (it's free).
데이터 엔지니어가 스트리밍 주문 데이터를 처리하기 위해 Lakeflow Spark 선언적 파이프라인을 설계하고 있습니다. 이 파이프라인은 Auto Loader를 사용하여 데이터를 수집하며, customer_id가 null이 아니고 amount가 0보다 큰지 확인하여 데이터 품질을 보장해야 합니다. 유효하지 않은 레코드는 삭제해야 합니다. Python을 사용하여 이 요구 사항을 구현하는 Lakeflow Spark 선언적 파이프라인 구성은 무엇일까요?
- A. @dlt.table
@dlt.expect( " valid_customer " , " customer_id IS NOT NULL " )
@dlt.expect( " valid_amount " , " amount > 0 " )
def silver_orders():
return dlt.read_stream( " bronze_orders " ) - B. @dlt.table
def silver_orders():
return dlt.read_stream( " bronze_orders " ) \
.expect_or_drop( " valid_customer " , " customer_id IS NOT NULL " ) \
.expect_or_drop( " valid_amount " , " amount > 0 " ) - C. @dlt.table
def silver_orders():
return dlt.read_stream( " bronze_orders " ) \
.expect( " valid_customer " , " customer_id IS NOT NULL " ) \
.expect( " valid_amount " , " amount > 0 " ) - D. @dlt.table
@dlt.expect_or_drop( " valid_customer " , " customer_id IS NOT NULL " )
@dlt.expect_or_drop( " valid_amount " , " amount > 0 " )
def silver_orders():
return dlt.read_stream( " bronze_orders " )
Correct Answer: D 🗳️
Explanation: Only visible for Free4Torrent 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 Free4Torrent members. You can sign-up / login (it's free).
컴퓨팅 리소스를 가장 많이 사용하는 사용자를 파악하기 위해 데이터 엔지니어링 팀은 Databricks 워크스페이스 내 사용량을 모니터링하여 리소스 활용도와 비용을 효율적으로 관리해야 합니다. 이를 위해 팀은 Unity Catalog의 시스템 카탈로그에서 제공되는 Databricks 시스템 테이블을 활용하여 워크스페이스 활동에 대한 자세한 정보를 얻기로 했습니다. 이 경우 시스템 카탈로그에서 어떤 SQL 쿼리를 실행해야 할까요?
- A. 선택하다
sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
시스템.청구.사용에서
사용자 이메일과 제품 번호(sku)를 기준으로 그룹화합니다.
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개 - B. 선택하다
sku_name,
usage_metadata.run_name AS user_email,
사용량 합계를 총 DBUS로 계산
시스템.청구.사용에서
사용자 이메일과 제품 번호(sku)를 기준으로 그룹화합니다.
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개 - C. 선택하다
identity_metadata.run_as AS user_email,
사용량 합계를 총 DBUS로 계산
시스템.청구.사용에서
사용자 이메일별로 그룹화
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개 - D. 선택하다
sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
시스템.청구.사용에서
사용자 이메일과 제품 번호(sku)를 기준으로 그룹화합니다.
총 DBS 값을 기준으로 내림차순 정렬
제한 수량 10개
Correct Answer: C 🗳️
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시설 모니터링 팀이 Delta 테이블의 device_readings 데이터를 기반으로 거의 실시간에 가까운 PowerBI 대시보드를 구축하고 있습니다.
열:
* device_id (문자열, 고유 센서 ID)
* event_ts (TIMESTAMP, 수집 타임스탬프 UTC)
* temperature_c (DOUBLE, °C 단위의 온도)
요구 사항:
* 각 센서에 대해 겹치지 않는 5분 간격으로 한 행씩 생성하되, 2분씩 오프셋합니다(예:
00:02-00:07, 00:07-00:12, ...).
* 각 행에는 구간 시작 시간, 구간 종료 시간 및 해당 구간의 평균 온도가 포함되어야 합니다.
* 하위 BI 도구(예: Power BI)는 시계열 막대를 표시하기 위해 간격 타임스탬프를 사용해야 합니다.
옵션:
- A. 버킷 AS (
device_id를 선택합니다.
window(event_ts, '5분', '2분', '5분') 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; - B. device_id를 선택합니다.
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 ' )); - C. device_id를 선택합니다.
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; - D. device_id를 선택합니다.
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;
Correct Answer: A 🗳️
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