Databricks Databricks-Certified-Data-Engineer-Professional Korean : Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version)

  • 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 08, 2026
  • Q & A: 250 Questions and Answers

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Databricks Databricks-Certified-Data-Engineer-Professional Korean Exam Syllabus Topics:

SectionWeightObjectives
Data Transformation, Cleansing, and Quality10%- Apply data cleansing and validation rules
- Enforce data quality standards
- Implement schema evolution and management
Data Governance7%- Use Unity Catalog for governance
- Enforce data policies and standards
- Manage data assets and metadata
Developing Code for Data Processing using Python and SQL22%- Use Databricks-specific libraries and APIs
- Implement complex data processing logic
- Write efficient and maintainable code
Data Ingestion & Acquisition7%- Use Auto Loader and structured streaming
- Handle incremental and batch data loads
- Ingest data from diverse sources
Data Sharing and Federation5%- Implement Lakehouse Federation
- Use Delta Sharing for secure data sharing
- Manage cross-platform data access
Data Modelling6%- Optimize table design and partitioning
- Implement dimensional and relational models
- Design Medallion Architecture
Cost & Performance Optimisation13%- Optimize compute and storage resources
- Improve query and pipeline performance
- Apply cost management best practices
Monitoring and Alerting10%- Set up alerts and notifications
- Monitor pipeline performance and health
- Track data lineage and metrics
Debugging and Deploying10%- Troubleshoot and debug pipelines
- Deploy using Asset Bundles, CLI, and APIs
- Implement CI/CD and DevOps practices
Ensuring Data Security and Compliance10%- Ensure data privacy and compliance
- Secure data at rest and in transit
- Implement access control and permissions

Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) Sample Questions:

Question #1

데이터 엔지니어가 파이썬 라이브러리를 사용하여 OpenAI를 호출하는 사기 탐지 파이프라인을 구축하고 있는데, API를 사용할 때 액세스 토큰이 필요합니다. 데이터 엔지니어는 어떤 Databricks CLI 명령어를 사용하여 비밀 키를 생성해야 할까요?

  • A. databricks 토큰 put-token KEY SCOPE; dbutils.secrets.get(KEY, SCOPE)
  • B. databricks secrets put-secret KEY SCOPE; dbutils.secrets.get(KEY, SCOPE)
  • C. databricks secrets put-secret SCOPE KEY; dbutils.secrets.get(SCOPE, KEY)
  • D. databricks 토큰 put-token SCOPE KEY; dbutils.tokens.get(SCOPE, KEY)
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Correct Answer: C  🗳️

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Question #2

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 단위 온도)
요구 사항:
각 센서에 대해 겹치지 않는 5분 간격으로 한 행씩 생성합니다.
간격은 2분 간격으로 표시됩니다(예: 00:02-00:07, 00:07-00:12, ...).
각 행에는 구간 시작, 구간 종료 및 평균값이 포함되어야 합니다.
해당 조각의 온도.
하위 BI 도구(예: Power BI)는 간격 타임스탬프를 사용해야 합니다.
시계열 막대 그래프를 그리기 위해.

  • A. 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, '5분', '2분'));
  • B. 버킷 AS (
    device_id를 선택합니다.
    window(event_ts, '5분', '2분', '5분') AS 승리,
    온도_c
    device_readings에서
    )
    device_id를 선택합니다.
    win.start AS 버킷_start,
    win.end AS 버킷_end,
    AVG(온도_섭씨) AS avg_temp_5m
    양동이에서
    device_id, win을 기준으로 그룹화
    device_id, bucket_start 순으로 정렬합니다.
  • C. 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;
  • D. device_id를 선택합니다.
    윈도우 시작을 버킷 시작으로,
    윈도우 끝을 버킷 끝으로,
    AVG(온도_섭씨) AS avg_temp_5m
    device_readings에서
    device_id, window(event_ts, '5 minutes', '5 minutes', '2 minutes')를 기준으로 그룹화하고 device_id, bucket_start를 기준으로 정렬합니다.
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Correct Answer: B  🗳️

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Question #3

태스크 오케스트레이터가 한 시간 간격으로 두 개의 작업을 실행하도록 구성되었습니다. 첫 번째 작업은 외부 시스템에서 Parquet 데이터를 /mnt/raw_orders/에 마운트된 디렉터리에 기록하는 것입니다. 이 데이터가 기록된 후, 다음 코드가 포함된 Databricks 작업이 실행됩니다.

customer_id와 order_id 필드가 각 주문을 고유하게 식별하는 복합 키 역할을 하고, time 필드는 해당 레코드가 소스 시스템에 대기열에 추가된 시간을 나타낸다고 가정합니다.
상위 시스템에서 단일 주문에 대해 몇 시간 간격으로 중복 항목이 대기열에 추가되는 경우가 종종 발생하는 것으로 알려져 있다면, 다음 중 어떤 설명이 정확합니까?

  • A. 모든 기록은 중복 제거 후 주문 테이블에 반영되기 전에 상태 저장소에 2시간 동안 보관됩니다.
  • B. 주문 테이블에는 중복된 항목이 없지만, 2시간 이상 늦게 도착한 기록은 무시되고 테이블에서 삭제됩니다.
  • C. 주문 테이블에는 최근 2시간 동안의 기록만 포함되며 중복된 항목은 없습니다.
  • D. 2시간 이상 간격을 두고 대기열에 추가된 중복 레코드는 유지될 수 있으며, 주문 테이블에는 customer_id와 order_id가 같은 중복 레코드가 포함될 수 있습니다.
  • E. 2시간 이상 간격을 두고 도착한 중복 기록은 삭제되지만, 동일한 배치에 도착한 중복 기록은 모두 주문 테이블에 기록될 수 있습니다.
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Correct Answer: D  🗳️

Question #4

레이크하우스 데이터베이스에 있는 customer_churn_params라는 테이블은 머신러닝 팀에서 고객 이탈 예측에 사용됩니다. 이 테이블에는 여러 상위 소스에서 가져온 고객 정보가 포함되어 있습니다. 현재 데이터 엔지니어링 팀은 상위 데이터 소스에서 가져온 최신 유효한 값으로 테이블을 덮어쓰는 방식으로 매일 밤 이 테이블을 업데이트하고 있습니다.
머신러닝 팀에서 사용하는 이탈 예측 모델은 실제 운영 환경에서 상당히 안정적입니다. 해당 팀은 지난 24시간 동안 변경된 기록에 대해서만 예측을 수행하는 데 관심이 있습니다.
어떤 접근 방식이 이러한 변경된 기록을 식별하는 것을 단순화할까요?

  • A. 현재의 덮어쓰기 로직을 ​​변경된 레코드만 수정하는 병합문으로 대체하고, 변경 데이터 피드를 통해 식별된 변경된 레코드에 대한 예측 로직을 작성합니다.
  • B. customer_churn_params 테이블의 모든 행에 이탈 예측 모델을 적용하되, 예측값이 변경되지 않은 행은 무시하고 예측 테이블에 업서트하는 로직을 구현하십시오.
  • C. 배치 작업을 전체 출력 모드를 사용하는 구조화된 스트리밍 작업으로 변환하고, 구조화된 스트리밍 작업이 customer_churn_params 테이블에서 데이터를 읽어 이탈 모델을 기반으로 점진적으로 예측하도록 구성합니다.
  • D. 새로운 예측을 하기 전에 고유 고객을 식별하는 키를 기준으로 이전 모델 예측과 현재 고객 이탈 매개변수 간의 차이를 계산합니다. 이전 예측에 포함되지 않은 고객에 대해서만 예측을 수행합니다.
  • E. 덮어쓰기 로직을 ​​수정하여 호출을 통해 채워진 필드를 포함하도록 합니다.
    데이터가 기록될 때 spark.sql.functions.current_timestamp() 필드를 사용하여 특정 날짜에 기록된 레코드를 식별할 수 있습니다.
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Correct Answer: A  🗳️

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Question #5

마케팅팀은 영업팀과 집계 테이블 형태로 데이터를 공유하려고 하지만, 두 팀에서 사용하는 필드 이름이 일치하지 않고, 마케팅 관련 필드 중 일부는 영업팀에서 승인받지 못한 상태입니다.
다음 중 간결성을 강조하면서 상황을 해결하는 방법은 무엇입니까?

  • A. CTAS 문을 사용하여 마케팅 테이블에서 파생 테이블을 생성하고, 변경 사항을 전파하기 위해 프로덕션 JON을 구성합니다.
  • B. 마케팅 테이블에서 영업팀에서 승인한 필드만 선택하여 뷰를 생성하고, 영업 명명 규칙에 따라 표준화해야 하는 필드 이름에는 별칭을 지정합니다.
  • C. 필요한 스키마로 새 테이블을 생성하고 Delta Lake의 DEEP CLONE 기능을 사용하여 한 테이블에 커밋된 변경 사항을 해당 테이블에 동기화합니다.
  • D. 마케팅 팀에게 결과를 CSV 파일로 다운로드하여 영업 조직에 이메일로 보내도록 지시하십시오.
  • E. 현재 운영 파이프라인에 병렬 테이블 쓰기 기능을 추가하여 마케팅 테이블의 요구 사항에 따라 달라지는 새로운 판매 테이블을 업데이트합니다.
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Correct Answer: B  🗳️

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