Microsoft Implementing Analytics Solutions Using Microsoft Fabric (DP-600 Korean Version) : DP-600 Korean Exam

  • Exam Code: DP-600-KR
  • Exam Name: Implementing Analytics Solutions Using Microsoft Fabric (DP-600 Korean Version)
  • Updated: Nov 12, 2026
  • Q & A: 212 Questions and Answers

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Microsoft DP-600 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • Maintain a data analytics solution: This section of the exam measures the skills of administrators and covers tasks related to enforcing security and managing the Power BI environment. It involves setting up access controls at both workspace and item levels, ensuring appropriate permissions for users and groups. Row-level, column-level, object-level, and file-level access controls are also included, alongside the application of sensitivity labels to classify data securely. This section also tests the ability to endorse Power BI items for organizational use and oversee the complete development lifecycle of analytics assets by configuring version control, managing Power BI Desktop projects, setting up deployment pipelines, assessing downstream impacts from various data assets, and handling semantic model deployments using XMLA endpoint. Reusable asset management is also a part of this domain.
Topic 2
  • Implement and manage semantic models: This section of the exam measures the skills of architects and focuses on designing and optimizing semantic models to support enterprise-scale analytics. It evaluates understanding of storage modes and implementing star schemas and complex relationships, such as bridge tables and many-to-many joins. Architects must write DAX-based calculations using variables, iterators, and filtering techniques. The use of calculation groups, dynamic format strings, and field parameters is included. The section also includes configuring large semantic models and designing composite models. For optimization, candidates are expected to improve report visual and DAX performance, configure Direct Lake behaviors, and implement incremental refresh strategies effectively.
Topic 3
  • Prepare data: This section of the exam measures the skills of engineers and covers essential data preparation tasks. It includes establishing data connections and discovering sources through tools like the OneLake data hub and the real-time hub. Candidates must demonstrate knowledge of selecting the appropriate storage type—lakehouse, warehouse, or eventhouse—depending on the use case. It also includes implementing OneLake integrations with Eventhouse and semantic models. The transformation part involves creating views, stored procedures, and functions, as well as enriching, merging, denormalizing, and aggregating data. Engineers are also expected to handle data quality issues like duplicates, missing values, and nulls, along with converting data types and filtering. Furthermore, querying and analyzing data using tools like SQL, KQL, and the Visual Query Editor is tested in this domain.

Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-600

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric (DP-600 Korean Version) Sample Questions:

Question #1

Microsoft Power BI Desktop에서 보고서와 시맨틱 모델을 만들고 있습니다.
값 측정값은 다음 그림과 같은 표현식을 갖습니다.

아래 그림에 제시된 정보를 바탕으로 각 문장을 완성하는 답을 드롭다운 메뉴에서 선택하세요.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Comprehensive Detailed Explanation
Step 1: Reviewing the provided DAX expression
SWITCH(
SELECTEDVALUE(Metric[Metric]),
" # of Customers " , " # " ,
" Gross Margin % " , " 0.00% " ,
" #,### "
)
This is applied as a dynamic format string.
It changes the display formatting of the Value measure depending on the selected metric.
Step 2: Identify the feature
The screenshot shows Format = Dynamic in the Power BI Modeling tab.
This confirms that a dynamic format string has been applied.
So, for the first blank # dynamic format string.
Step 3: Identify the output formats
If the metric = # of Customers # format is " # " # whole numbers.
If the metric = Gross Margin % # format is " 0.00% " # percentages.
Fallback # " #,### " # also whole numbers.
Thus, the measure supports both whole numbers and percentages.
So, for the second blank # percentages or whole numbers.
References
Dynamic format strings in Power BI
SWITCH function in DAX

Question #2

Workspace 1이라는 Fabric 작업 영역에 Dataflow1이라는 데이터 흐름이 있습니다. Dataflow1에는 2,000개의 행을 반환하는 쿼리가 있습니다. 다음 그림과 같이 Power Query에서 해당 쿼리를 볼 수 있습니다.

pickupLongitude 열에서 무엇을 확인할 수 있습니까?

  • A. 테이블의 모든 행이 프로파일링됩니다.
  • B. 한 번만 나타나는 값이 935개 있습니다.
  • C. 해당 열에 중복된 값이 있습니다.
  • D. 해당 열에 결측값이 있습니다.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

Model 1이라는 이름의 시맨틱 모델이 있습니다. Model 1에는 가져오기 모드를 사용하는 테이블이 다섯 개 있습니다. 또한 Model 1에는 HR이라는 이름의 동적 행 수준 보안(RLS) 역할이 있습니다. HR 역할은 인사 담당자가 자신이 소속된 부서의 데이터만 볼 수 있도록 직원 데이터를 필터링합니다.
Model1을 Fabric 테넌트에 게시하고 RLS 역할 멤버십을 구성합니다. 그런 다음 모델과 관련 보고서를 사용자에게 공유합니다.
인사 담당자가 보고서에 나타난 데이터가 불완전하다고 보고했습니다.
인사 담당자가 확인한 데이터의 유효성을 검증하려면 어떻게 해야 할까요?

  • A. 인사 관리자로서 보고서를 보려면 역할을 '테스트'로 선택하세요.
  • B. HR 담당자 역할로 데이터를 보려면 '테스트' 역할을 선택하세요.
  • C. 보고서의 데이터를 인사부의 의도된 필터 논리에 맞게 필터링합니다.
  • D. 인사 담당자에게 Microsoft Power BI Desktop에서 보고서를 열어달라고 요청하세요.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

유형 1 느리게 변화하는 차원(SCD)에 대한 데이터 로딩 패턴을 생성해야 합니다.
이 과정에 어떤 두 가지 행동을 포함해야 할까요? 각 정답은 해결책의 일부를 나타냅니다.
참고: 정답 하나당 1점입니다.

  • A. 비키 속성이 변경될 때 행을 업데이트합니다.
  • B. 차원 테이블에 자연 키가 존재하고 비키 속성 값이 변경된 경우 새 행을 삽입합니다.
  • C. 테이블에서 자연 키가 새 값일 때 새 레코드를 삽입합니다.
  • D. 비키 속성 값이 변경될 때 행의 유효 종료일을 업데이트합니다.
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

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Question #5

Fabric 테넌트에 Lakehouse1이라는 이름의 호숫가 주택 데이터가 있습니다. Lakehouse1에는 8개의 열로 구성된 Delta 테이블이 있습니다. 이제 기존 8개 열에 2개의 열이 추가된 새로운 데이터를 받게 됩니다.
Spark DataFrame을 생성하고 해당 DataFrame을 df라는 변수에 할당합니다. DataFrame에는 새 데이터가 포함되어 있습니다. 다음 요구 사항을 충족하기 위해 새 데이터를 Delta 테이블에 추가해야 합니다.
* 기존 행을 모두 유지합니다.
* 모든 새로운 데이터가 표에 추가되었는지 확인하십시오.
코드를 어떻게 완성해야 할까요? 정답을 선택하려면 답변란에서 적절한 옵션을 고르세요.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

o add new data to the Delta table while meeting the specified requirements:
You should use the append mode to ensure that all new data is added to the table without affecting the existing rows.
You should set the mergeSchema option to true to allow the schema of the Delta table to be updated with the new columns found in the DataFrame.
The completed code would look like this:
df.write.format( " delta " ).mode( " append " )
option( " mergeSchema " , " true " )
saveAsTable( " Lakehouse1.TableName " )

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