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Microsoft DP-600 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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q199-Q204):

NEW QUESTION # 199
You have a Fabric tenant that contains a workspace named Workspace1 and a user named User1. Workspace1 contains a warehouse named DW1.
You share DW1 with User1 and assign User1 the default permissions for DW1.
What can User1 do?

Answer: A

Explanation:
Assigning a user default permission ("Read") to a warehouse in a Fabric workspace enables them to connect to the SQL analytics endpoint, which is the equivalent of CONNECT permissions in SQL Server. However, this permission only allows them to connect; it doesn't grant them the ability to query tables, views, functions, or stored procedures within the warehouse unless they're also given access to those specific objects through T-SQL GRANT statements.
In essence, the default "Read" permission provides the necessary connectivity to the warehouse's SQL analytics endpoint, but it does not automatically grant access to the data and objects within the warehouse itself. Further permissions need to be granted through T-SQL or Fabric's workspace roles and item permissions system.
Reference:
https://learn.microsoft.com/en-us/fabric/data-warehouse/share-warehouse-manage-permissions


NEW QUESTION # 200
You have a Fabric tenant that contains a workspace named Workspace_DEV. Workspace_DEV contains the semantic models shown in the following table.

Workspace_DEV contains the dataflows shown in the following table.

Answer:

Explanation:

Explanation:


NEW QUESTION # 201
You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?

Answer: A


NEW QUESTION # 202
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.
You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 203
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

Explanation:
Before specifying the partition column in the Destination settings of the Copy activity, you should set Mode to Append (A). This will allow the Copy activity to add data to the table while taking the partition column into account. References = The configuration options for Copy activities and partitioning in Azure Data Factory, which are applicable to Fabric dataflows, are outlined in the official Azure Data Factory documentation.


NEW QUESTION # 204
......

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