DP-600 Implementing Analytics Solutions Using Microsoft Fabric Questions and Answers

To create a diagram that contains only the Sales table and related tables, you should use the Model view (C) in Microsoft Power BI Desktop. This view allows you to visualize and manage the relationships between tables within your semantic model. References = Microsoft Power BI Desktop documentation outlines the functionalities available in Model view for managing semantic models.

To execute each set of items in sequential order with minimized development effort and complexity, you should use the following options:

Orchestration pipeline: Use a pipeline with an Invoke pipeline activity. This allows for orchestrating and scheduling the execution of other pipelines, ensuring they run in the correct sequence.

Bronze layer: Implement a pipeline Copy activity. This aligns with the table indicating that the Bronze layer uses pipelines with Copy activities for data integration.

Silver layer: Implement a pipeline Dataflow activity. The table specifies that Dataflows are used for the Silver layer.

Gold layer: Implement a pipeline Stored procedure activity. Stored procedures are specified for the Gold layer according to the table.

The Spark engine will read only the 'SalesOrderNumber', 'OrderDate', 'CustomerName', 'UnitPrice' columns from Sales_raw.csv. - Yes

Removing the partition will reduce the execution time of the query. - No

Adding inferSchema='true' to the options will increase the execution time of the query. - Yes

The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.

The two languages you can use to perform model scoring in a Fabric notebook using the predict function are Spark SQL (option C) and PySpark (option D). These are both part of the Apache Spark ecosystem and are supported for machine learning tasks in a Fabric environment. References = You can find more information about model scoring and supported languages in the context of Fabric notebooks in the official documentation on Azure Synapse Analytics.

The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B. The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included. References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.

For the HighestSellingPrice, you should use the GREATEST function to find the highest value from the given price columns. However, T-SQL does not have a GREATEST function as found in some other SQL dialects, so you would typically use a CASE statement or an IIF statement with nested MAX functions. Since neither of those are provided in the options, you should select MAX as a placeholder to indicate the function that would be used to find the highest value if combining multiple MAX functions or a similar logic was available.

For the TradePrice, you should use the COALESCE function, which returns the first non-null value in a list. The COALESCE function is the correct choice as it will return AgentPrice if it's not null; if AgentPrice is null, it will check WholesalePrice, and if that is also null, it will return ListPrice.

The complete code with the correct SQL functions would look like this:

SELECT ProductID,

MAX(ListPrice, WholesalePrice, AgentPrice) AS HighestSellingPrice, -- MAX is used as a placeholder

COALESCE(AgentPrice, WholesalePrice, ListPrice) AS TradePrice

FROM Sales.Products

Select MAX for HighestSellingPrice and COALESCE for TradePrice in the answer area.

To create a calculation item that changes the context from the selected date to month-to-date (MTD), the appropriate DAX expression involves using the CALCULATE function to alter the filter context and the DATESMTD function to specify the month-to-date context.

The correct completion for the DAX expression would be:

In the first dropdown, select CALCULATE.

In the second dropdown, select SELECTEDMEASURE.

This would create a DAX expression in the form:

CALCULATE(

SELECTEDMEASURE(),

DATESMTD('Date'[DateColumn])

)

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.

Folding in Power Query refers to operations that can be translated into source queries. In this case, "some" of the steps can be folded, which means that some transformations will be executed at the data source level. The steps that cannot be folded will be executed within the Power Query engine. Custom steps, especially those that are not standard query operations, are usually executed within Power Query engine rather than being pushed down to the source system.

References =

Query folding in Power Query

Power Query M formula language

The measure should use the AVERAGE function to calculate the average value.

It should reference the Response Value column from the 'Survey' table.

The 'Number of months' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

Data Engineers: Read All SQL analytics endpoint data

Data Analysts: Read All Apache Spark

Data Scientists: Read All SQL analytics endpoint data

The permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:

Data Engineers need read and write access but not to datasets or reports.

Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.

Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.