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Microsoft DP-700 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement and manage an analytics solution | 30-35% | - Implement lifecycle management and source control for analytics assets - Implement security and governance - Configure and manage workspace settings and item access - Orchestrate processes with Data Factory pipelines |
| Topic 2: Ingest and transform data | 30-35% | - Optimize and manage Delta tables - Design and implement loading patterns - Ingest data using Data Factory pipelines and Dataflows Gen2 - Transform data using Dataflows Gen2 and notebooks - Ingest and transform data using eventstreams |
| Topic 3: Monitor and optimize an analytics solution | 30-35% | - Optimize performance of data storage and queries - Implement error handling in pipelines - Monitor Fabric items |
Questions Candidates Ask About the Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700 Korean Version)
Plan on needing 700 / 1000 to pass the DP-700 Korean exam, and budget $165 USD for the official registration. Treat that fee as single-use: a failed attempt means paying the same amount again for a retake. The cheapest insurance policy is preparation — cycle through the 154 practice questions at TorrentExam until your timed mock results clear the passing mark consistently, and only then schedule your exam.
The Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700 Korean Version) blueprint spans 3 domains. The heaviest hitters include Implement and manage an analytics solution (30-35%), Ingest and transform data (30-35%), and Monitor and optimize an analytics solution (30-35%). The complete breakdown — every domain and subtopic — is laid out in the syllabus section above, and that is the map your study sessions should follow.
Expect 50 - 60 questions and 100 minutes on the clock for the DP-700 Korean exam. Time management is a skill of its own here: candidates who untimed-study often discover the real pressure only when the countdown starts. Build that muscle memory early — run full timed simulations in the TorrentExam desktop or online test engine, practice flagging and skipping, and let pacing become reflex before exam day.
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Formally known as the Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700 Korean Version), the DP-700 Korean exam is Microsoft's way of certifying that you can apply its technologies to real-world problems — pass it, and you hold the Microsoft Certified: Fabric Data Engineer Associate certification, a Associate-level credential. Because it is vendor-backed and skills-driven, employers treat it as proof rather than promise. It also feeds into a wider path that includes Microsoft Certified: Fabric Data Engineer Associate.
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No specific prior certification required.
Requirements like these evolve, so before locking in an exam date, double-check the current rules on the official Microsoft exam page.
Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700 Korean Version) Sample Questions:
Workspace1이라는 이름의 Fabric 워크스페이스가 있고, 그 안에 Lakehouse1이라는 이름의 호숫가 주택 모델이 있습니다.
PySpark 기반 데이터 변환 프로세스를 자동화해야 합니다. 솔루션은 다음 요구 사항을 충족해야 합니다.
* 사용자가 PySpark 코드를 대화형으로 편집하고 테스트할 수 있도록 하십시오.
* 코드가 자동 실행될 수 있도록 구현 가능한지 확인하십시오.
어떤 세 가지 행동을 순서대로 수행해야 할까요?
Correct Answer:

Explanation:
The correct sequence is Create a notebook # Attach the notebook to Lakehouse1 # Create a data pipeline
.
First, create a Fabric notebook because notebooks provide the interactive development environment required for authoring, executing, debugging, and testing PySpark code. Microsoft describes Fabric notebooks as a primary code item for developing Apache Spark workloads and specifically supports their use for data ingestion, preparation, and transformation.
Second, attach the notebook to Lakehouse1 . Attaching a lakehouse establishes the notebook ' s lakehouse context, allowing PySpark code to access the lakehouse ' s Files and Tables through Spark and OneLake. This enables developers to interactively validate the transformation against the required Fabric data.
Third, create a data pipeline and use a Notebook activity to operationalize the notebook. Fabric Data Factory explicitly supports running Fabric notebooks as pipeline activities for automated data transformation workflows. Pipelines can subsequently be run manually, scheduled, or incorporated into larger orchestration processes.
Converting the notebook to an Apache Spark job definition is unnecessary; Spark job definitions are separate executable Fabric items. Publishing the notebook is also not required for pipeline execution.
Study Guide reference: Ingest and transform data # Transform data with Apache Spark # Fabric notebooks # Orchestrate processes and schedules with Data Factory pipelines.
이벤트하우스와 Database1이라는 KQL 데이터베이스가 포함된 Fabric 워크스페이스가 있습니다. Database1에는 다음 내용이 있습니다.
Table1이라는 이름의 테이블
Table2라는 이름의 테이블
Policy1이라는 이름의 업데이트 정책
Policy1은 Table1의 데이터를 Table2로 전송합니다.
다음은 표2의 데이터 예시입니다.
최근 Table1에 대해 다음과 같은 작업이 수행되었습니다.
StreamData 열에 temperature라는 추가 요소가 추가되었습니다.
타임스탬프 열의 데이터 형식이 날짜로 변경되었습니다.
DeviceId 열의 데이터 형식이 문자열로 변경되었습니다.
Table2에 추가 레코드를 로드할 계획입니다.
Table1에서 Table2로 로드될 두 개의 레코드는 무엇입니까? 각 정답은 완전한 풀이 과정을 나타냅니다.
참고: 정답 하나당 1점입니다.
- A.

- B.

- C.

- D.

Correct Answer: A,D 🗳️
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
Fabric을 사용하여 다음 세 가지 데이터 세트를 처리할 계획입니다.
* 데이터셋1: 이 데이터셋은 Fabric에 추가되며 소스와 대상 간에 고유한 기본 키를 갖습니다. 고유한 기본 키는 정수이며 1부터 시작하여 1씩 증가합니다.
* 데이터셋2: 이 데이터셋은 대량 데이터 전송을 사용하는 반정형 데이터를 포함합니다. 소스와 대상 간의 데이터셋 처리는 하나의 프로세스로 이루어져야 합니다. 데이터 변환 프로세스에는 개발 모드에서 데이터셋을 이해하고 활용하기 위한 사용자 지정 시각화 도구 사용이 포함됩니다.
* 데이터셋3. 이 데이터셋은 테이크하우스에 있습니다. 데이터는 일괄적으로 로드될 예정입니다. 데이터 변환 과정에는 로드 중에 행 기반 윈도우 함수가 포함됩니다.
데이터 세트에 사용할 항목 유형을 식별해야 합니다. 솔루션은 개발 노력을 최소화하고 가능한 경우 내장 기능을 활용해야 합니다. 각 데이터 세트에 대해 어떤 항목을 식별해야 할까요? 답변하려면 답변 영역에서 적절한 옵션을 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:

Explanation:
제품 치수를 생성해야 합니다.
Apache Spark SQL 코드를 어떻게 완성해야 할까요? 정답을 선택하려면 답변란에서 적절한 옵션을 고르세요.
참고: 정답 하나당 1점입니다.
Correct Answer:

Explanation:
A screenshot of a computer Description automatically generated
Join between Products and ProductSubCategories:
Use an INNER JOIN.
The goal is to include only products that are assigned to a subcategory. An INNER JOIN ensures that only matching records (i.e., products with a valid subcategory) are included.
Join between ProductSubCategories and ProductCategories:
Use an INNER JOIN.
Similar to the above logic, we want to include only subcategories assigned to a valid product category. An INNER JOIN ensures this condition is met.
WHERE Clause
Condition: IsActive = 1
Only active products (where IsActive equals 1) should be included in the gold layer. This filters out inactive products.
Amazon S3 버킷의 데이터 사용이 기술 요구 사항을 충족하는지 확인해야 합니다.
어떻게 해야 할까요?
- A. 바로가기를 만들고 작업 공간에 캐싱이 활성화되어 있는지 확인하십시오.
- B. 워크스페이스 ID를 생성하고 노트북의 높은 동시 접속을 활성화합니다.
- C. 워크스페이스 ID를 생성하고 데이터 파이프라인에서 해당 ID를 사용합니다.
- D. 바로가기를 만들고 작업 공간에 대한 캐싱이 비활성화되어 있는지 확인하십시오.
Correct Answer: D 🗳️
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