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DP-600 Test Preparation & DP-600 Updated Test Cram

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q117-Q122):

NEW QUESTION # 117
You have a Fabric tenant that contains a warehouse. The warehouse uses row-level security (RLS). You create a Direct Lake semantic model that uses the Delta tables and RLS of the warehouse. When users interact with a report built from the model, which mode will be used by the DAX queries?

A. DirectQuery
B. Dual
C. Direct Lake
D. Import

Answer: A

Explanation:
When users interact with a report built from a Direct Lake semantic model that uses row-level security (RLS), the DAX queries will operate in DirectQuery mode (A). This is because the model directly queries the underlying data source without importing data into Power BI. Reference = The Power BI documentation on DirectQuery provides detailed explanations of how RLS and DAX queries function in this mode.

NEW QUESTION # 118
You have a custom Direct Lake semantic model named Model1 that has one billion rows of data.
You use Tabular Editor to connect to Model1 by using the XMLA endpoint.
You need to ensure that when users interact with reports based on Model1, their queries always use Direct Lake mode.
What should you do?

A. From Model, configure the Default Mode option.
B. From Model, configure the Storage Location option.
C. From Partitions, configure the Mode option.
D. From Model, configure the Direct Lake Behavior option.

Answer: D

Explanation:
Click on Semantic model.
In the Properties pane, choose the Direct Lake behavior for your custom Direct Lake semantic model:
Automatic: This is the default behavior. It allows Direct Lake with fallback to DirectQuery mode if data can't be efficiently loaded into memory.
Direct Lake only: This option ensures no fallback to DirectQuery mode
https://powerbi.microsoft.com/en-au/blog/leveraging-pure-direct-lake-mode-for-maximum-query- performance/

NEW QUESTION # 119
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression: df.explain()
Does this meet the goal?

A. No
B. Yes

Answer: A

NEW QUESTION # 120
You have a Fabric tenant that contains a warehouse.
You use a dataflow to load a new dataset from OneLake to the warehouse.
You need to add a Power Query step to identify the maximum values for the numeric columns.
Which function should you include in the step?

A. Table.Range
B. Table.Profile
C. Table. MaxN
D. Table.Max

Answer: D

NEW QUESTION # 121
You to need assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.
Which additional permissions should you assign when you share the data store? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Comprehensive Detailed Explanation
When assigning permissions to roles in a Fabric workspace for analytics workloads, you must align permissions with the responsibilities and tools typically used by each persona:
1. Data Engineers
Their primary tasks include building and transforming data pipelines, as well as making datasets usable by others.
The appropriate permission is Build Reports on the default dataset, because this allows them to create and manage reports using the curated semantic model while preparing data for analysts and scientists.
2. Data Analysts
Analysts work heavily with Apache Spark for exploratory analysis, cleansing, and shaping data.
The correct permission is Read All Apache Spark, as this grants them the ability to query and analyze Spark- based data directly without elevated permissions that are unnecessary for their role.
3. Data Scientists
Data scientists often use SQL analytics endpoints to run queries, train models, and integrate data into machine learning workflows.
The correct permission is Read All SQL analytics endpoint data, since it enables direct programmatic access to the SQL endpoint, which is needed for advanced modeling and experimentation.
Summary of Selections:
DataEngineers # Build Reports on the default dataset
DataAnalysts # Read All Apache Spark
DataScientists # Read All SQL analytics endpoint data
References (Microsoft Fabric - DP-600 exam scope):
Workspace roles and permissions in Microsoft Fabric
SQL analytics endpoint in Fabric
Apache Spark in Microsoft Fabric

NEW QUESTION # 122
......

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