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Microsoft DP-600 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
  • 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 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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q163-Q168):

NEW QUESTION # 163
You have a Microsoft Power Bl semantic model that contains a table named Date. Date contains the following data.

You need to ensure that the visuals that use the Date table appear in chronological order. The solution must minimize administrative effort.
What should you do?

Answer: D


NEW QUESTION # 164
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?

Answer: C


NEW QUESTION # 165
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Table1. Table1 contains the following data.

You need to perform the following actions:
- Load the data from Table1 into a star schema.
- Create a product dimension table named DimProduct and a fact table
named FactSales.
Which three columns should you include in DimProduct?

Answer: B


NEW QUESTION # 166
You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2.
Workspace1 is used as the development environment.
Workspace2 is used as the production environment.
Each environment uses a different storage account.
Workspace1 contains a Dataflow Gen2 named Dataflow1. The data source of Dataflow1 is a CSV file in blob storage.
You plan to implement a deployment pipeline to deploy items from Workspace1 to Workspace2.
You need to ensure that the data source references the correct location in the production environment.
What should you do?

Answer: A

Explanation:
Scenario:
Dev = Workspace1 with Dataflow Gen2 (source = blob storage CSV).
Prod = Workspace2, different storage account.
Need: ensure deployed dataflow points to the production storage location.
Analysis:
Data source rules: used to remap data sources between environments (e.g., blob storage dev # blob storage prod).
Parameter rules: used when the data source location is parameterized (for example, a parameter storing the file path or connection string).
Best practice: use parameters in the dataflow for connection strings, then apply parameter rules in deployment pipelines.
In this case, since the requirement is about ensuring the reference updates correctly, only parameter rules are needed (not data source rules).


NEW QUESTION # 167
You need to create a DAX measure to calculate the average overall satisfaction score.
How should you complete the DAX code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 168
......

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