Free DP-500 Exam Braindumps (page: 17)

Page 17 of 46

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 question, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are using an Azure Synapse Analytics serverless SQL pool to query a collection of Apache Parquet files by using automatic schema inference. The files contain more than 40 million rows of UTF-8-encoded business names, survey names, and participant counts. The database is configured to use the default collation.

The queries use OPENROWSET and infer the schema shown in the following table.



You need to recommend changes to the queries to reduce I/O reads and tempdb usage.

Solution: You recommend defining a data source and view for the Parquet files. You recommend updating the query to use the view.

Does this meet the goal?

  1. Yes
  2. No

Answer(s): A

Explanation:

"views are generally faster and have more features such as OPENROWSET"
"External tables require an explicit defined schema while views can use OPENROWSET to provide automatic schema inference allowing for more flexibility (but note that an explicitly defined schema can provide faster performance)"


Reference:

https://www.jamesserra.com/archive/2020/11/external-tables-vs-t-sql-views-on-files-in-a-data-lake/



HOTSPOT (Drag and Drop is not supported)
You are creating a Power BI Desktop report.
You add a Python visual to the report page.
You plan to create a scatter chart to visualize the data.
You add Python code to the Python script editor.
You need to create the scatter chart.

How should you complete the Python code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: matplotlib.pyplot
Create a scatter plot
Let's create a scatter plot to see if there's a correlation between age and weight.

Under Paste or type your script code here, enter this code:

import matplotlib.pyplot as plt
dataset.plot(kind='scatter', x='Age', y='Weight', color='red')
plt.show()

Box 2: chart.show()


Reference:

https://docs.microsoft.com/en-us/power-bi/connect-data/desktop-python-visuals#create-a-scatter-plot



You are configuring a Power BI report for accessibility as shown in the following table.




You need to change the default colors of all three visuals to make the report more accessible to users who have color vision deficiency.

Which two settings should you configure in the Customize theme window? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

  1. Theme colors
  2. Divergent colors
  3. Sentiment colors
  4. First-level elements colors

Answer(s): B,C


Reference:

https://docs.microsoft.com/en-us/power-bi/create-reports/desktop-report-themes



You use Azure Synapse Analytics and Apache Spark notebooks to explore native visuals.

You need to use PySpark to gain access to the visual libraries.

Which Python libraries should you use?

  1. Seaborn and TensorFlow
  2. Seaborn only
  3. Matplotlib and Seaborn
  4. Matplotlib only
  5. Matplotlib and TensorFlow
  6. TensorFlow only

Answer(s): C

Explanation:

Matplotlib
You can render standard plotting libraries, like Matplotlib, using the built-in rendering functions for each library.
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy.

Additional libraries
Beyond these libraries, the Azure Synapse Analytics Runtime also includes the following set of libraries that are often used for data visualization:

Seaborn
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-data-visualization
https://seaborn.pydata.org/



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