Databricks : Databricks-Certified-Professional-Data-Engineer Korean Questions & Answers
Last Updated: Oct 09, 2026
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Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Auto Loader and streaming ingestion - Connecting to diverse data sources |
| Data Modelling | 6% | - Medallion Architecture implementation - Delta Lake table design - Schema design and management |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Access control and permissions - Compliance standards implementation |
| Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors - CI/CD and DevOps practices |
| Monitoring and Alerting | 10% | - Performance and health monitoring - Setting up alerts and notifications - Pipeline observability and logging |
| Data Governance | 7% | - Policy enforcement - Data lineage and metadata tracking - Unity Catalog management |
| 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 Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
Databricks-Certified-Professional-Data-Engineer Korean Exam: The FAQ Candidates Actually Need
Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) is an official Databricks certification exam, listed under the code Databricks-Certified-Professional-Data-Engineer Korean. Passing it earns you the Databricks Certified Professional Data Engineer certification, positioned at the Professional level. It also connects to Databricks Certified Associate Data Engineer. For anyone aiming at a leadership track in IT, this credential is one of the clearest markers of verified skill.
Passing Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) takes 70%, and the official registration fee is USD 200. Retakes charge the full USD 200 again, which is why accuracy in preparation matters more than volume. Validate your readiness with repeated ActualTorrent practice scores above the requirement before committing to a date.
The Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) exam contains 59 questions to answer within 120 minutes. The SOFT engine from ActualTorrent imitates the real test scene on your computer and uses special methods to help you master questions and answers, so the official time limit becomes a practiced routine rather than a surprise.
No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended
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Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) is structured into 10 official domains. The leading ones are Developing Code for Data Processing using Python and SQL (22%), Ensuring Data Security and Compliance (10%), and Data Ingestion & Acquisition (7%). The full topic breakdown appears above; accurate preparation starts with an accurate map.
Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) Sample Questions:
델타 호수에 해당하는 것은 무엇일까요?
- A. 기본 키 및 외래 키 제약 조건을 활용하여 차원 테이블에 중복 값이 입력되지 않도록 할 수 있습니다.
- B. Z-ORDER는 Delta Lake 테이블에 저장된 숫자 값에만 적용할 수 있습니다.
- C. Delta Lake는 각 테이블의 처음 32개 열에 대한 통계를 자동으로 수집하며, 이는 쿼리 필터에 따라 데이터 건너뛰기에 활용됩니다.
- D. 레이크하우스의 뷰는 소스 테이블의 최신 버전에 대한 유효한 캐시를 항상 유지합니다.
Correct Answer: C 🗳️
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마케팅팀은 영업팀과 집계 테이블 형태로 데이터를 공유하려고 하지만, 두 팀에서 사용하는 필드 이름이 일치하지 않고, 마케팅 관련 필드 중 일부는 영업팀의 승인을 받지 못했습니다.
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. 현재 운영 파이프라인에 병렬 테이블 쓰기 기능을 추가하여 마케팅 테이블의 요구 사항에 따라 달라지는 새로운 판매 테이블을 업데이트합니다.
- C. 필요한 스키마로 새 테이블을 생성하고 Delta Lake의 DEEP CLONE 기능을 사용하여 한 테이블에 커밋된 변경 사항을 해당 테이블에 동기화합니다.
- D. 마케팅 테이블에서 영업팀에서 승인한 필드만 선택하여 뷰를 생성하고, 영업 명명 규칙에 따라 표준화해야 하는 필드 이름에는 별칭을 지정합니다.
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. CTAS 및 RTAS 진술
- B. spark.writestream.format( ' delta ' ).mode( ' append ' )
- C. spark.write.format( ' delta ' ).mode( ' append ' )
- D. INSERT INTO 작업
Correct Answer: B 🗳️
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다음 오류 추적(display(df.select(3* " heartrate " )))에서 AnalysisException: cannot resolve ' heartrateheartrateheartrate '이 표시됨)이 나타났을 때, 발생한 오류를 설명하는 문장은 무엇입니까?
- A. 테이블에 heartrateheartrateheartrate라는 이름의 열이 없습니다.
- B. 컬럼 객체를 곱할 수 없으므로 형식 오류가 발생했습니다.
- C. 심박수 열이 열로 올바르게 인식되지 않아 구문 오류가 발생했습니다.
- D. DataFrame 객체는 곱셈을 할 수 없으므로 타입 오류가 발생했습니다.
Correct Answer: A 🗳️
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데이터 엔지니어링 팀은 다음 코드를 유지 관리합니다.
이 코드가 논리적으로 올바른 결과를 생성하고 원본 테이블의 데이터가 중복 제거 및 유효성 검사를 거쳤다고 가정할 때, 이 코드가 실행될 때 발생하는 상황을 설명하는 문장은 무엇입니까?
- A. silver_customer_sales 테이블은 gold_customer_lifetime_sales_summary 테이블의 모든 레코드에서 계산된 집계 값으로 일괄 처리되어 덮어쓰여집니다.
- B. 배치 작업이 gold_customer_lifetime_sales_summary 테이블을 업데이트합니다. 이때 customer_id를 기본 키로 사용하여 현재 테이블 버전과 값이 다른 행만 교체합니다.
- C. 증분 작업은 silver_customer_sales 테이블에 새 행이 기록되었는지 감지합니다. 새 행이 감지되면 모든 집계가 다시 계산되어 gold_customer_lifetime_sales_summary 테이블을 덮어씁니다.
- D. 증분 작업은 상태 저장소의 실행 정보를 활용하여 gold_customer_lifetime_sales_summary 테이블의 집계 값을 업데이트합니다.
- E. gold_customer_lifetime_sales_summary 테이블은 silver_customer_sales 테이블의 모든 레코드에서 계산된 집계 값으로 일괄 처리되어 덮어쓰여집니다.
Correct Answer: E 🗳️
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