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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q65-Q70):

NEW QUESTION # 65
You have a Fabric tenant that contains lakehouse named Lakehousel. Lakehousel contains a Delta table with eight columns. You receive new data that contains the same eight columns and two additional columns.
You create a Spark DataFrame and assign the DataFrame to a variable named df. The DataFrame contains the new data. You need to add the new data to the Delta table to meet the following requirements:
* Keep all the existing rows.
* Ensure that all the new data is added to the table.
How should you complete the code? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

o add new data to the Delta table while meeting the specified requirements:
* You should use the append mode to ensure that all new data is added to the table without affecting the existing rows.
* You should set the mergeSchema option to true to allow the schema of the Delta table to be updated with the new columns found in the DataFrame.
The completed code would look like this:
df.write.format("delta").mode("append")
option("mergeSchema", "true")
saveAsTable("Lakehouse1.TableName")

NEW QUESTION # 66
You have a Fabric tenant that contains JSON files in OneLake. The files have one billion items.
You plan to perform time series analysis of the items.
You need to transform the data, visualize the data to find insights, perform anomaly detection, and share the insights with other business users. The solution must meet the following requirements:
- Use parallel processing.
- Minimize the duplication of data.
- Minimize how long it takes to load the data.
What should you use to transform and visualize the data?

A. the pandas library in a Fabric notebook
B. the PySpark library in a Fabric notebook
C. a Microsoft Power BI report that uses core visuals

Answer: B

Explanation:
PySpark provides robust capabilities for data transformation and manipulation, making it well- suited for preparing data for time series analysis. You can then use libraries like pandas for further data manipulation if needed and leverage Spark's machine learning capabilities for anomaly detection.

NEW QUESTION # 67
You have a Microsoft Power Bl report named Report1 that uses a Fabric semantic model.
Users discover that Report1 renders slowly.
You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table.

What will provide the greatest reduction in the rendering duration of Report1?

A. Change the visual type of Orders By Dale.
B. Enable automatic page refresh.
C. Optimize the DAX query of Orders By Date by using DAX Studio.
D. Reduce the number of visuals in Report1.

Answer: D

Explanation:
Based on the duration breakdown provided, the major contributor to the rendering duration is categorized as "Other," which is significantly higher than DAX Query and Visual display times. This suggests that the issue is less likely with the DAX calculation or visual rendering times and more likely related to model performance or the complexity of the visual. However, of the options provided, optimizing the DAX query can be a crucial step, even if "Other" factors are dominant. Using DAX Studio, you can analyze and optimize the DAX queries that power your visuals for performance improvements. Here's how you might proceed:
Open DAX Studio and connect it to your Power BI report.
Capture the DAX query generated by the Orders By Date visual.
Use the Performance Analyzer feature within DAX Studio to analyze the query.
Look for inefficiencies or long-running operations.
Optimize the DAX query by simplifying measures, removing unnecessary calculations, or improving iterator functions.
Test the optimized query to ensure it reduces the overall duration.

NEW QUESTION # 68
You have a Microsoft Power Bl semantic model.
You plan to implement calculation groups.
You need to create a calculation item that will change the context from the selected date to month-to-date (MTD).
How should you complete the DAX expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

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])
)

NEW QUESTION # 69
Hotspot Question
You have a Fabric warehouse that contains two tables named DimDate and Trips.
DimDate contains the following fields.

Trips contains the following fields.

You need to compare the average miles per trip for statutory holidays versus non-statutory holidays.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 70
......

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