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Microsoft DP-600 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Analytics Solutions Using Microsoft Fabric
Exam Number:DP-600
Exam Format:Case studies, Multiple-choice, Drag-and-drop
Exam Price:$165 USD
Certificate Validity Period:1 year
Exam Duration:120 minutes
Available Languages:English, Chinese (Simplified), Korean, Japanese
Related Certifications:Microsoft Certified: Fabric Analytics Engineer Associate
Passing Score:700/1000
Real Exam Qty:40-60
Sample Questions:Microsoft DP-600 Sample Questions
Exam Way:Online proctored or in-person testing center
Pre Condition:Candidates should have foundational knowledge of data concepts, experience with Microsoft Fabric, and proficiency in data transformation and modeling. Familiarity with Power BI is recommended but not required.
Official Syllabus URL:https://learn.microsoft.com/en-us/certifications/exams/dp-600

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Microsoft DP-600 시험요강:

주제소개
주제 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.
주제 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.
주제 3
  • 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.

최신 Microsoft Certified DP-600 무료샘플문제 (Q94-Q99):

질문 # 94
You plan to implement a Fabric workspace that will save a curated dataset to Delta tables in OneLake. The dataset will contain only structured tables named by using enforced relationships.
The ingestion process must apply multi-table updates as a single unit of work. The developers at your company will use T-SQL for ingestion and transformations.
You need to recommend an analytical data store that supports read/write T-SQL operations and full multi- table transactional guarantees.
What should you recommend?

정답：C


질문 # 95
Drag and Drop Question
You are building a solution by using a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df. The DataFrame returns four columns.
You need to change the data type of a string column named Age to integer. The solution must return a DataFrame that includes all the columns.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

정답：

설명：


질문 # 96
You have a Fabric tenant that uses a Microsoft Power BI Premium capacity.
You need to enable scale-out for a semantic model.
What should you do first?

정답：D

설명：
https://learn.microsoft.com/en-us/power-bi/enterprise/service-premium-scale-out-configure


질문 # 97
You have the source data model shown in the following exhibit.

The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：


질문 # 98
You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
* Support the use of dataflows to load and append data to the data store.
* Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

정답：A,D

설명：
A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically. References = The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.


질문 # 99
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