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Exams evolve; your material should evolve faster. Lead1Pass revises the Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) question bank continuously, and every Databricks-Certified-Professional-Data-Engineer Korean purchase in 2026 carries 365 days of free updates.
Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion | 15-20% | - Streaming ingestion
|
| Topic 2: Data Processing with Spark | 25-30% | - Spark DataFrames and Spark SQL
|
| Topic 3: Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Topic 4: Delta Lake | 20-25% | - Delta Lake operations
|
| Topic 5: Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
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The Databricks-Certified-Professional-Data-Engineer Korean exam is how Databricks decides who earns the Databricks Certification certification, positioned at the Professional level. It checks whether you can apply the Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) objectives to realistic problems — the skill employers are actually hiring for. It also sits within a broader certification ecosystem covering Databricks Certified Associate Developer, Databricks Certified Data Analyst Associate, so the effort compounds across your career. In a crowded global market, that badge is a competitive edge you can point to.
The Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) blueprint spans 5 domains, led by Delta Lake (20-25%), Data Warehouse and Lakehouse Architecture (15-20%), and Pipeline Development and Orchestration (10-15%). Use the weightings as a budget: heavier domains earn heavier slices of your week. The full topic breakdown is in the exam topics section above — plan against it, not against hunches.
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Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) Sample Questions:
데이터 엔지니어는 PySpark를 사용하여 각 store_id별로 event_ts 값을 기준으로 정렬된 sale_amount의 3개 이벤트 이동 평균을 계산해야 합니다. 각 store 내에서 event_ts 값은 고유합니다.
엔지니어는 다음과 같은 코드를 작성합니다.
`from pyspark.sql import Window`, `import pyspark.sql.functions as Fw = Window.partitionBy("store_id").orderBy("event_ts")`, `df_result = df.withColumn("moving_avg", F.avg("sale_amount").over(w))` 코드를 실행한 후, 엔지니어는 몇몇 행을 무작위로 검사하여 `moving_avg`가 해당 행들에 대해 수동으로 계산한 3개 이벤트 평균과 일치하지 않는다는 것을 발견했습니다.
데이터 엔지니어는 윈도우 사양을 수정하기 위해 무엇을 해야 할까요?
- A. 윈도우 지정에 .rangeBetween(-2, 0)을 추가하여 프레임이 현재 행 이전의 두 단위의 숫자 범위를 포함하도록 하고 평균을 세 이벤트로 제한합니다.
- B. 윈도우 지정에 .rowsBetween(-2, 0)을 추가하여 프레임이 현재 행과 이전 두 행을 포함하도록 하고 평균을 3개 이벤트로 제한합니다.
- C. orderBy("event_ts")를 partitionBy("store_id", "event_ts")로 변경하여 각 행이 자체 파티션을 형성하고 평균이 정확히 세 행으로 제한되도록 합니다.
- D. F.avg("sale_amount").over(w)를 groupBy("store_id").agg(F.avg("sale_amount"))로 바꾸고 원래 DataFrame에 다시 조인하여 범위를 제한합니다.
Correct Answer: B 🗳️
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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. 데이터 불균형을 효율적으로 처리하고, 기존 데이터를 덮어쓰지 않고 클러스터링 키를 변경할 수 있으며, 변화하는 쿼리 패턴에 적응할 수 있는 유동적 클러스터링을 활성화합니다.
- B. Hive 스타일 파티셔닝을 사용하세요. 효율적인 데이터 건너뛰기 기능을 제공하며 언제든지 파티션 열을 쉽게 변경할 수 있습니다.
- C. 쿼리 패턴이 변경될 때 유연성을 극대화하고 유지 관리를 최소화하기 위해 파티셔닝과 Z-순서를 결합합니다.
- D. Z-순서를 적용하세요. 기존 파일을 수정하지 않고도 데이터 레이아웃을 유연하게 재구성할 수 있고, 새로운 필터 열에 쉽게 적용할 수 있습니다.
Correct Answer: A 🗳️
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데이터 엔지니어가 소속 조직을 위한 안전한 데이터 공유 전략을 수립하고 있습니다. 회사는 민감한 고객 분석 데이터를 두 개의 서로 다른 파트너와 공유해야 합니다.
파트너 A는 Unity Catalog가 활성화된 Databricks를 사용하고, 파트너 B는 Databricks 없이 AWS에서 Apache Spark를 사용합니다.
이러한 상황에서 기업은 어떻게 안전한 데이터 공유를 구현해야 할까요?
- A. D2O(Open Sharing Protocol)는 D2D(Direct-to-Direct) 공유보다 보안성이 뛰어나므로 양측 파트너 모두 D2O를 사용해야 합니다. 베어러 토큰 방식은 Unity Catalog의 기본 인증 방식보다 항상 더 안전합니다.
- B. 보안 요구 사항이 동일하므로 양측 파트너는 동일한 델타 공유 방식을 사용해야 합니다. 양측 파트너 모두 베어러 토큰을 생성하고 호환성을 극대화하기 위해 개방형 공유 프로토콜(D2O)을 사용하십시오.
- C. Databricks 간 공유(D2D)는 동일한 클라우드 제공업체 내에서만 사용할 수 있으므로, 클라우드 간 공유 시나리오에서는 개방형 공유(D2O)를 사용해야 합니다. 외부 플랫폼과 공유할 때는 Unity Catalog 거버넌스를 사용할 수 없습니다.
- D. 파트너 A의 경우, Unity Catalog 통합을 통해 토큰 교환 없이 Databricks 간 공유(D2D)를 구현합니다. 파트너 B의 경우, 베어러 토큰 또는 OIDC 페더레이션 인증을 사용하는 개방형 공유 프로토콜(D2O)을 활용하고, 두 방식 모두 강력한 보안 및 거버넌스를 유지하도록 합니다.
Correct Answer: D 🗳️
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거래 테이블이 product_id, user_id 및 event_date 열을 기준으로 Liquid 클러스터링되었습니다.
쓰기 시 클러스터링을 지원하지 않는 작업은 무엇입니까?
- A. spark.write.format( ' delta ' ).mode( ' append ' )
- B. spark.writestream.format( ' delta ' ).mode( ' append ' )
- C. CTAS 및 RTAS 진술
- D. INSERT INTO 작업
Correct Answer: B 🗳️
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데이터 파이프라인은 구조화된 스트리밍 방식을 사용하여 Kafka에서 Delta Lake로 데이터를 수집합니다. 데이터는 Bronze 테이블에 저장되며, Kafka에서 생성된 타임스탬프, 키, 값이 포함됩니다. 파이프라인 배포 후 3개월이 지난 시점에서 데이터 엔지니어링 팀은 특정 시간대에 지연 현상이 발생하는 것을 발견했습니다.
선임 데이터 엔지니어가 Delta 테이블의 스키마와 데이터 수집 로직을 업데이트하여 Apache Spark에서 기록한 현재 타임스탬프와 Kafka 토픽 및 파티션을 포함시켰습니다. 팀은 추가된 메타데이터 필드를 사용하여 일시적인 처리 지연을 진단할 계획입니다.
이 문제를 진단하는 과정에서 팀은 어떤 제약에 직면하게 될까요?
- A. 새로운 필드는 과거 기록에 대해 계산되지 않습니다.
- B. Spark는 Kafka 소스에서 토픽 파티션 필드를 가져올 수 없습니다.
- C. 테이블 스키마를 업데이트하면 Delta 트랜잭션 로그 메타데이터가 무효화됩니다.
- D. 테이블 스키마를 업데이트하려면 추가되는 각 파일에 대해 기본값을 제공해야 합니다.
Correct Answer: A 🗳️
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