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NEW QUESTION # 56
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows. You run the following T-SQL statement.
CREATE TABLE test.FactSales AS CLONE OF Dbo.FactSales;
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:
Explanation:
A replica of dbo.Sales is created in the test schema by copying the metadata only. - Yes Additional schema changes to dbo.FactSales will also apply to test.FactSales. - No Additional data changes to dbo.FactSales will also apply to test.FactSales. - No The CREATE TABLE AS CLONE statement creates a copy of an existing table, including its data and any associated data structures, like indexes. Therefore, the statement does not merely copy metadata; it also copies the data. However, subsequent schema changes to the original table do not automatically propagate to the cloned table. Any data changes in the original table after the clone operation will not be reflected in the clone unless explicitly updated.
References =
CREATE TABLE AS SELECT (CTAS) in SQL Data Warehouse
NEW QUESTION # 57
You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.
You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?
Answer: A
Explanation:
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.
NEW QUESTION # 58
You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.
You need to write a DAX query that will be executed by using the XMLA endpoint The query must return a table of stores that have opened since December 1,2023.
How should you complete the DAX expression? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct order for the DAX expression would be:
* DEFINE VAR _SalesSince = DATE ( 2023, 12, 01 )
* EVALUATE
* FILTER (
* SUMMARIZE ( Store, Store[Name], Store[OpenDate] ),
* Store[OpenDate] >= _SalesSince )
In this DAX query, you're defining a variable _SalesSince to hold the date from which you want to filter the stores. EVALUATE starts the definition of the query. The FILTER function is used to return a table that filters another table or expression. SUMMARIZE creates a summary table for the stores, including the Store[Name] and Store[OpenDate] columns, and the filter expression Store[OpenDate] >= _SalesSince ensures only stores opened on or after December 1, 2023, are included in the results.
References =
* DAX FILTER Function
* DAX SUMMARIZE Function
NEW QUESTION # 59
You have a Microsoft Power Bl project that contains a file named definition.pbir. definition.pbir contains the following JSON.
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:
Explanation:
We are analyzing the JSON for definition.pbir in a Power BI Project:
{
" version " : " 1.0 " ,
" datasetReference " : {
" byPath " : {
" path " : " ../Sales.Dataset "
},
" byConnection " : null
}
}
Statement 1:
" definition.pbir is in the PBIR-Legacy format. "
No # The legacy PBIR format references datasets by byConnection (pointing to the Power BI service).
Here, it uses byPath, which is the new project format, not legacy.
Statement 2:
" The semantic model referenced by definition.pbir is located in the Power BI service. " No # The JSON shows byPath: " ../Sales.Dataset " , meaning the semantic model is referenced locally within the project folder (Sales.Dataset), not in the Power BI service.
Statement 3:
" When the related report is opened, Power BI Desktop will open the semantic model in full edit mode. " Yes # Since the semantic model is referenced by path (local PBIP project files), Power BI Desktop will open the model in full edit mode. If it had been byConnection, it would open in live connect mode to the service instead.
Final Answer:
PBIR-Legacy format # No
Semantic model in Power BI service # No
Opens in full edit mode # Yes
References:
Power BI Project (PBIP) structure
PBIR formats: byPath vs byConnection
NEW QUESTION # 60
You have a Fabric notebook that has the Python code and output shown in the following exhibit.
Which type of analytics are you performing?
Answer: A
Explanation:
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of "what has happened" by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
References: Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.
NEW QUESTION # 61
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