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Databricks Databricks-Certified-Data-Engineer-Professional Korean Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Professional Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Professional |
| Passing Score: | 70% |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 45-60 |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Exam Format: | Multiple choice, Proctored online exam, Scenario-based questions, Multiple select |
| Exam Price: | $200 USD |
| Recommended Training: | Delta Lake Fundamentals Databricks Data Engineer Learning Path |
| Exam Registration: | Databricks Certification Portal Databricks Academy |
| Sample Questions: |  |
| Exam Way: | Online proctored exam via Databricks certification platform |
| Pre Condition: | Recommended: Databricks Certified Data Engineer Associate or equivalent hands-on experience with Spark and Delta Lake |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Databricks-Certified-Data-Engineer-Professional Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Delta Lake and Data Management | - Schema evolution and enforcement - Time travel and versioning - Delta Lake transactions and ACID properties |
| Production Pipelines and Orchestration | - Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows |
| Databricks Lakehouse Platform Architecture | - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture |
| Data Modeling and Transformation | - Spark SQL transformations - Performance optimization techniques - Dimensional modeling concepts |
| Data Ingestion and Processing | - ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader |
Databricks Databricks-Certified-Data-Engineer-Professional Korean Exam: Worthy Answers
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The Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) is delivered Online proctored exam via Databricks certification platform — pick the arrangement that suits you when booking.
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The Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) is Databricks's certification exam for Databricks Certified Data Engineer Professional, at the Professional level. In drastic competition, the certificate is an explicit demonstration of your knowledge. Related credentials include Databricks Certified Data Engineer Associate.
The Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) blueprint spans 5 domains — including Databricks Lakehouse Platform Architecture, Delta Lake and Data Management, Data Ingestion and Processing. Progress domain by domain at your own level; the complete outline above lists every subtopic.
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Recommended: Databricks Certified Data Engineer Associate or equivalent hands-on experience with Spark and Delta Lake Eligibility rules change over time, so verify the current requirements on the official page (official Databricks-Certified-Data-Engineer-Professional Korean exam page) before registering.
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional Korean Version) Sample Questions:
데이터 엔지니어는 manufacturing-team이라는 그룹에 액세스 권한을 부여해야 합니다. 이 팀은 quality 스키마에 테이블을 생성할 수 있는 권한이 필요합니다. manufacturing-team 그룹이 manufacturing이라는 상위 카탈로그를 가진 production 스키마에 테이블을 생성할 수 있도록 최소한의 권한으로 허용하는 SQL 명령은 무엇일까요?
- A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
- B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
- C. 제조팀에게 제조 품질 스키마에 대한 테이블 사용 권한을 부여합니다. 제조팀에게 제조 품질 스키마에 대한 스키마 사용 권한을 부여합니다. 제조팀에게 제조 카탈로그에 대한 카탈로그 사용 권한을 부여합니다.
- D. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
Correct Answer: D 🗳️
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데이터 엔지니어가 프로덕션 데이터셋의 일부를 샌드박스 환경으로 복사하는 PySpark 코드를 검토하면서 개인 식별 정보(PII)가 복사되지 않는지 확인해야 합니다. 판매 테이블을 확인한 결과, 사용자 이메일이 유일한 PII 데이터이며 사용자를 식별하는 유일한 열이라는 것을 발견했습니다.
pyspark.sql에서 함수를 F로 가져옵니다.
원하는 결과를 얻으려면 어떤 익명화 코드를 사용해야 할까요?
- A. df.withColumn("user_email", F.sha2("user_email"))
- B. df.withColumn("hashed_email", sha2("user_email"))
- C. df.withColumn("user_email", F.regexp_replace("user_eamail", "@*", "@anonymized.com"))
- D. df.withColumn("user_emai", F.expr("uuid()"))
Correct Answer: A 🗳️
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데이터 엔지니어가 정적 사용자 조회 테이블의 값과 스트리밍 DataFrame(streamingDF)의 값을 결합하기 위해 조인 작업을 수행하고 있습니다.
어떤 코드 블록이 유효하지 않은 스트림-정적 조인을 시도합니까?
- A. streamingDF.join(userLookup, ["userid"], how="inner")
- B. streamingDF.join(userLookup, ["user_id"], how="outer")
- C. userLookup.join(streamingDF, ["userid"], how="inner")
- D. streamingDF.join(userLookup, ["user_id"], how="left")
- E. userLookup.join(streamingDF, ["user_id"], how="right")
Correct Answer: B 🗳️
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다음 중 Delta Lake Auto Compaction을 설명하는 문장은 무엇입니까?
- A. 쓰기 작업이 완료된 후 비동기 작업이 실행되어 파일을 추가로 압축할 수 있는지 확인합니다. 압축할 수 있는 경우 기본값인 128MB를 목표로 최적화 작업이 실행됩니다.
- B. 최적화된 쓰기는 디렉터리 파티션 대신 논리 파티션을 사용합니다. 파티션 경계는 메타데이터에만 표현되므로 더 적은 수의 작은 파일이 기록됩니다.
- C. 쓰기 작업이 완료된 후 비동기 작업이 실행되어 파일을 추가로 압축할 수 있는지 확인합니다. 압축할 수 있는 경우 기본값인 1GB를 목표로 최적화 작업이 실행됩니다.
- D. 작업 클러스터가 종료되기 전에 가장 최근 작업 중에 수정된 모든 테이블에 대해 최적화가 실행됩니다.
- E. 데이터는 메모리에 직접 커밋하는 대신 메시징 버스에 대기열에 저장됩니다. 작업이 완료되면 메시징 버스에 있는 모든 데이터가 한 번에 커밋됩니다.
Correct Answer: A 🗳️
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신입 데이터 엔지니어에게 DataFrame df를 사용하여 그룹화된 집계를 수행하는 스트리밍 데이터 파이프라인을 개발하라는 요청이 있었습니다. 이 파이프라인은 겹치지 않는 5분 간격으로 평균 습도와 평균 온도를 계산해야 합니다. 또한, 늦게 도착하는 데이터의 경우 10분 동안 증분 상태 정보를 유지해야 합니다.
스트리밍 데이터프레임 df는 다음과 같은 스키마를 가지고 있습니다.
"장치 ID INT, 이벤트 시간 TIMESTAMP, 온도 FLOAT, 습도 FLOAT"
코드 블록:
이 과제를 완료하려면 코드 블록 안의 빈칸을 올바르게 채우는 답을 선택하세요.
- A. slidingWindow("event_time", "10분")
- B. delayWrite("event_time", "10분")
- C. withWatermark("event_time", "10분")
- D. awaitArrival("event_time", "10분")
- E. await("event_time + `10분'")
Correct Answer: C 🗳️
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