Microsoft Implementing Analytics Solutions Using Microsoft Fabric (DP-600 Korean Version) - DP-600 Korean Exam Questions

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Explanation:
PBIR-Legacy format # No
Located in Power BI service # No
Opens in full edit mode # Yes
Comprehensive Detailed Explanation
We are analyzing a Power BI Report (PBIR) definition file in JSON format:
{
" version " : " 1.0 " ,
" byPath " : {
" path " : " ../Sales.Dataset "
}
}
}
Step 1: Is this PBIR-Legacy?
PBIR-Legacy format is used when referencing a dataset hosted in the Power BI service (cloud).
In this case, the JSON uses byPath , which references a local dataset file ( Sales.Dataset ) in the project folder , not a service dataset.
Therefore: No , it is not PBIR-Legacy.
Step 2: Where is the semantic model located?
The datasetReference points to ../Sales.Dataset .
That means the semantic model is stored locally in the project structure , not in the Power BI service.
Therefore: No , it is not located in the Power BI service.
Step 3: What happens when opening in Power BI Desktop?
Since the dataset reference is local ( byPath ), Power BI Desktop opens the semantic model in full edit mode (allowing schema, relationships, measures, etc. to be modified).
If it were PBIR-Legacy with a service model, only Live Connection mode would be available.
Therefore: Yes , Power BI Desktop opens it in full edit mode.
References
PBIR file structure in Power BI projects
Semantic model references: byPath vs byConnection
PBIR-Legacy format # No
Located in Power BI service # No
Opens in full edit mode # Yes

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Explanation:

Comprehensive Detailed Explanation
Step 1: Understanding the requirement
We need a KQL query that:
Reads data from Weatherdata .
Orders by Datetime .
Calculates Delta_temperature as the difference between the current row and the preceding row .
Returns the final columns: Datetime, Lat, Long, Temperature, Delta_temperature .
Step 2: Evaluate the provided statements
Weatherdata # Starts the query with the source table.
| sort by Datetime asc # Ensures chronological order, required for prev() to work correctly.
| extend Delta_temperature = Temperature - prev(Temperature) # Computes difference between the current row and the previous row's temperature.
| project Datetime, Lat, Long, Temperature, Delta_temperature # Selects the required output columns.
Step 3: Incorrect choices (to avoid)
next(Temperature,1) # Would calculate the difference with the next row , not the previous.
prev(Temperature,2) # Would skip one row, giving the difference with two rows back, which is not needed.
summarize ... by Datetime # Aggregates the data; not required here since we just need row-level differences.
Step 4: Final Correct Sequence
Weatherdata
| sort by Datetime asc
| extend Delta_temperature = Temperature - prev(Temperature)
| project Datetime, Lat, Long, Temperature, Delta_temperature
References
prev() function in KQL
extend operator in KQL
project operator in KQL

Explanation:

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Explanation:

Comprehensive Detailed Explanation
Workspace Roles in Microsoft Fabric (same as Power BI workspaces):
Viewer
Can only read/view content.
Cannot create or edit.
Contributor
Can create and write data (datasets, reports, semantic models).
Cannot manage permissions or workspace settings.
Member
Can create and edit content.
Can also add/remove people to the workspace (except Admins).
Cannot delete the workspace itself.
Admin
Full control: can configure settings, add/remove members, and delete the workspace.
Step 1: Group1 Requirements
Must be able to write data .
Must NOT be able to add members.
# Contributor (can write but cannot manage membership).
Step 2: Group2 Requirements
Must be able to configure and maintain workspace settings .
# Only Admin role allows configuring workspace-level settings.
# Admin .
Step 3: Group3 Requirements
Must be able to write data .
Must be able to add members .
Must NOT be able to delete the workspace.
# This matches the Member role.

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

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