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Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Professional Data Engineer Exam
Exam Number:Databricks-Certified-Professional-Data-Engineer
Real Exam Qty:60
Passing Score:70%
Exam Format:Multiple Choice, Multiple Select
Related Certifications:Databricks Certified Data Analyst Associate
Databricks Certified Associate Developer
Exam Price:$200 USD
Exam Duration:90 minutes
Available Languages:English
Certificate Validity Period:2 years
Sample Questions: DOWNLOAD DEMO
Exam Way:Online proctored exam (Pearson VUE)
Pre Condition:Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended
Official Syllabus URL:https://www.databricks.com/learn/certification/professional-data-engineer

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

SectionWeightObjectives
Topic 1: Delta Lake20-25%- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Optimize and Z-order
  • 3. Time travel and data versioning
- Delta Lake operations
  • 1. Delta Live Tables
  • 2. Schema evolution and enforcement
  • 3. Merge, update, delete operations
Topic 2: Data Processing with Spark25-30%- Spark DataFrames and Spark SQL
  • 1. DataFrame operations and transformations
  • 2. Window functions
  • 3. Spark SQL queries and functions
- Python and SQL for data engineering
  • 1. Performance optimization techniques
  • 2. Spark APIs in Python
  • 3. Built-in and user-defined functions
Topic 3: Data Ingestion15-20%- Batch ingestion methods
  • 1. Integration with external systems
  • 2. Spark APIs for ingestion
  • 3. DBR autoloader
- Streaming ingestion
  • 1. Structured streaming fundamentals
  • 2. Kafka integration
Topic 4: Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Task dependencies and orchestration
  • 2. Monitoring and alerting
  • 3. Jobs and job scheduling
Topic 5: Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Bronze, silver, gold data layers
  • 2. Data governance fundamentals
  • 3. Differences between data lake, data warehouse, and lakehouse

Thinking About the Databricks-Certified-Professional-Data-Engineer Korean Exam? Read This First

The official Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) blueprint is split into 5 domains, starting with Data Processing with Spark (25-30%), Data Ingestion (15-20%), Pipeline Development and Orchestration (10-15%). The percentages tell you where the exam concentrates its questions, so budget your study time accordingly — the complete outline above lists every domain and subtopic.

The Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) is Databricks's official exam for the Databricks Certification certification, positioned at the Professional level. It is the credential hiring managers scan for, and passing it proves your skills meet the vendor's own standard. It also links to related certifications like Databricks Certified Associate Developer, Databricks Certified Data Analyst Associate, so this exam can serve as a building block in a longer certification plan.

The official registration fee is $200 USD, and you will need 70% to pass. Here is the part people forget: fail, and the retake costs the same full fee again — there is no discount for a second attempt. Drill with the 217 practice questions from ActualTestsQuiz until your scores are consistently past the mark, and only then spend that fee.

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The exam contains 60 questions to be finished within 90 minutes. That ratio is tighter than most candidates expect, which is why pacing matters as much as knowledge. Practice skipping and returning to hard questions, set yourself a per-question time budget, and complete at least two full timed mock exams in the ActualTestsQuiz engine so you feel the clock pressure before it actually counts.

Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended Vendor requirements do change from time to time, so confirm the current criteria before scheduling — the official Databricks-Certified-Professional-Data-Engineer Korean exam page has the final word.

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

Question #1

데이터 엔지니어링 팀은 Delta Lake 테이블의 값을 모니터링하기 위해 Databricks SQL 쿼리와 알림을 구성했습니다. recent_sensor_recordings 테이블에는 최근 5분 동안의 기록에 대한 센서 ID, 타임스탬프 및 온도가 포함되어 있습니다.
아래 쿼리는 알림을 생성하는 데 사용됩니다.

해당 쿼리는 1분마다 갱신되도록 설정되어 있으며 항상 10초 이내에 완료됩니다. 평균 온도(온도)가 120°F(섭씨 약 38도)를 초과하면 알림이 발생하도록 설정되어 있습니다. 알림은 최대 1분 간격으로 전송됩니다.
이 알림이 3분 동안 연속으로 발생하다가 멈춘다면, 다음 중 어떤 설명이 반드시 참이어야 합니까?

  • A. 모든 센서의 평균 온도가 쿼리를 3회 연속 실행했을 때 120도를 초과했습니다.
  • B. 적어도 하나의 센서에서 기록된 평균 온도가 쿼리를 3회 연속 실행했을 때 120도를 초과했습니다.
  • C. 소스 쿼리가 3분 동안 제대로 업데이트되지 않은 후 다시 시작되었습니다.
  • D. recent_sensor_recordings 테이블이 쿼리를 세 번 연속 실행하는 동안 응답하지 않았습니다.
  • E. 하나 이상의 센서에서 기록된 최고 온도가 쿼리를 3회 연속 실행했을 때 120도를 초과했습니다.
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Correct Answer: B  🗳️

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Question #2

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.view
    def raw_customers():
    return spark.format.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.json( " s3://my-bucket/raw-customers/ " )
  • D. import dlt
    @dlt.table
    def raw_customers():
    return spark.read.format( " parquet " ).load( " s3://my-bucket/raw-customers/ " )
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Correct Answer: C  🗳️

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Question #3

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. COMMIT 명령이 실행될 때까지 아무 일도 일어나지 않습니다.
  • B. 해당 테이블은 카탈로그에서 제거되지만 데이터는 저장소에 남아 있습니다.
  • C. Delta Lake에서 운영 데이터 삭제를 방지하므로 오류가 발생합니다.
  • D. 해당 테이블은 카탈로그에서 제거되고 데이터는 삭제됩니다.
  • E. 데이터는 삭제된 것으로 표시되지만 타임 트래블을 통해 복구할 수 있습니다.
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Correct Answer: D  🗳️

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Question #4

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. CTAS 및 RTAS 진술
  • C. INSERT INTO 작업
  • D. spark.writestream.format( ' delta ' ).mode( ' append ' )
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Correct Answer: D  🗳️

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Question #5

Databricks Query Profiler에서 쿼리 실행을 검토하던 데이터 엔지니어는 '상위 연산자' 패널에서 '정렬' 연산자의 소요 시간 및 메모리 사용량이 높게 나타나는 것을 확인했습니다. 또한 Spark UI에서도 데이터 스필링이 빈번하게 발생하는 것으로 보고되었습니다.
데이터 엔지니어는 이 문제를 어떻게 해결해야 할까요?

  • A. 메모리 사용량을 줄이려면 브로드캐스트 조인으로 전환하세요.
  • B. 데이터 분산을 개선하기 위해 셔플 파티션 수를 늘립니다.
  • C. 정렬 작업을 필터 작업으로 변환합니다.
  • D. 정렬하기 전에 DataFrame을 단일 파티션으로 재분할합니다.
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Correct Answer: B  🗳️

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