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Microsoft DP-600 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

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:

평균 전체 만족도 점수를 계산하는 DAX 측정값을 만들어야 합니다.
DAX 코드를 어떻게 완성해야 합니까? 답하려면 답변 영역에서 적절한 옵션을 선택하십시오.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
The measure should use the AVERAGE function to calculate the average value.
It should reference the Response Value column from the ' Survey ' table.
The ' Number of months ' should be used to define the period for the average calculation.
To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

Topic 2, Contoso, ltd.
Overview
Contoso, ltd. is a US-based health supplements company, Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.
Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroupi and ReseachReviewefsGfoup2.
Data Environment
Contoso has the following data environment
* The Sales division uses a Microsoft Power B1 Premium capacity.
* The semantic model of the Online Sales department includes a fact table named Orders that uses import mode. In the system of origin, the OrderlD value represents the sequence in which orders are created.
* The Research department uses an on-premises. third-party data warehousing product.
* Fabric is enabled for contoso.com.
* An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Producthne1. The data is in the delta format.
* A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.
Planned Changes
Contoso plans to make the following changes:
* Enable support for Fabric in the Power Bl Premium capacity used by the Sales division.
* Make all the data for the Sales division and the Research division available in Fabric.
* For the Research division, create two Fabric workspaces named Producttmelws and Productline2ws.
* in Productlinelws. create a lakehouse named LakehouseV
* In Lakehouse1. create a shortcut to storage1 named ResearchProduct.
Data Analytics Requirements
Contoso identifies the following data analytics requirements:
* All the workspaces for the Sales division and the Research division must support all Fabric experiences.
* The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
* The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
* For the Research division workspaces, the members of ResearchRevtewersGroupl must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
* For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
* All the semantic models and reports for the Research division must use version control that supports branching Data Preparation Requirements Contoso identifies the following data preparation requirements:
* The Research division data for Producthne2 must be retrieved from Lakehouset by using Fabric notebooks.
* All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.
Semantic Model Requirements
Contoso identifies the following requirements for implementing and managing semantic models;
* The number of rows added to the Orders table during refreshes must be minimized.
* The semantic models in the Research division workspaces must use Direct Lake mode.
General Requirements
Contoso identifies the following high-level requirements that must be considered for all solutions:
* Follow the principle of least privilege when applicable
* Minimize implementation and maintenance effort when possible.

Model1이라는 이름의 시맨틱 모델을 포함하는 Fabric 테넌트가 있습니다.
Modell에는 TemperatureUnit이라는 테이블이 있습니다. TemperatureUnit 테이블에는 Unit이라는 열이 있습니다. Unit 열에는 섭씨와 화씨에 대한 수동으로 추가된 옵션이 포함되어 있습니다.
모델1에는 다음과 같은 측정값이 포함되어 있습니다.

사용자가 미터법(섭씨)과 영국식(화씨) 단위 간에 표시 값을 전환할 수 있도록 [TemperatureDisplay] 측정값에 동적 형식 문자열을 추가해야 합니다.
DAX 표현식을 어떻게 완성해야 합니까? 답하려면 답변 영역에서 적절한 옵션을 선택하십시오.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Warehouse!라는 이름의 웨어하우스가 있는 Fabric 워크스페이스가 있습니다. Warehouse에는 다음 데이터가 포함되어 있습니다.

테이블을 비정규화하고 결과에 ContractType 및 StartDate 속성을 포함하는 T-SQL 문을 작성해야 합니다. 이 솔루션은 다음 요구 사항을 충족해야 합니다.
* 계약 테이블에서 일치하는 행의 속성을 포함합니다.
* 직원 테이블의 모든 행이 보존되도록 하십시오.
* 직원이 2명 이상인 모든 계약 유형에 대해 계약 유형별 총 직원 수를 반환합니다.
문장을 어떻게 완성해야 할까요? 답하려면 답변란에서 적절한 옵션을 선택하세요.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Comprehensive Detailed Explanation
We are tasked with writing a T-SQL query to denormalize the tables Employee and Contract in a Fabric warehouse.
Requirements Breakdown
Include attributes from matching rows in the Contract table
This means we must join Employee and Contract on EmployeeID.
Ensure that all rows from the Employee table are preserved
This requires a LEFT OUTER JOIN from Employee to Contract. If an employee has no contract, we still want the employee record included.
Return the total number of employees per contract type for all contract types that have more than two employees This means we need to:
Group by ContractType
Count distinct EmployeeID
Filter groups where that count > 2.
To filter aggregated results, we use HAVING, not WHERE.
Completed Query
WITH result AS (
SELECT
e.EmployeeID,
e.EmployeeName,
e.EmployeePosition,
c.ContractType,
c.StartDate
FROM Employee AS e
LEFT OUTER JOIN Contract AS c
ON c.EmployeeID = e.EmployeeID
)
SELECT
ContractType,
COUNT(DISTINCT EmployeeID) AS TotalEmployees
FROM result
GROUP BY ContractType
HAVING COUNT(DISTINCT EmployeeID) > 2;
Why This is Correct
LEFT OUTER JOIN ensures all employees are included, regardless of contracts.
HAVING allows filtering aggregated groups by employee counts.
The query returns ContractType, StartDate (as requested in denormalization), and aggregated employee counts.
References
T-SQL JOINs in Microsoft Fabric
Aggregate functions and HAVING clause
Denormalization best practices in Fabric warehouses

PySpanc를 사용하여 Fabric 노트북에서 고객 구매를 분석하고 있습니다. 다음과 같은 DataFrame이 있습니다.

customer_id 열을 기준으로 DataFrame들을 조인해야 합니다. 데이터 셔플링을 최소화하는 솔루션을 작성하세요. 다음 코드를 작성하십시오.

결과 데이터프레임을 채우려면 어떤 코드를 실행해야 합니까?

  • A.
  • B.
  • C.
  • D.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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다음과 같은 KQL 쿼리가 있습니다.

다음 각 문장에 대해, 문장이 사실이면 '예'를 선택하고, 그렇지 않으면 '아니오'를 선택하십시오. 참고: 각 정답은 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

We are analyzing this KQL query:
Sales
| where Status != " Cancelled "
| where OrderDate > = ago(30d)
| summarize TotalSales = sum(SalesAmount) by ProductCategory
| where TotalSales > 0
Statement 1:
" The query excludes sales that have a Status of Cancelled. "
Yes # where Status != " Cancelled " filters them out.
Statement 2:
" The query calculates the total sales of each product category for the last 30 days. " Yes # where OrderDate > = ago(30d) ensures only last 30 days.
summarize TotalSales = sum(SalesAmount) by ProductCategory groups by category.
Statement 3:
" The query includes product categories that have had zero sales during the last 30 days. " No # The final filter where TotalSales > 0 excludes categories with zero sales.
Final Answer:
Excludes Cancelled sales # Yes
Calculates total sales by product category for last 30 days # Yes
Includes product categories with zero sales # No
References:
KQL where operator
KQL summarize operator

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