Practice Free DP-500 Exam Online Questions

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

From Power Query Editor, you profile the data shown in the following exhibit.

The loT GUID and loT ID columns are unique to each row in the query.

You need to analyze loT events by the hour and day of the year. The solution must improve dataset performance.

Solution: You split the loT DateTime column into a column named Date and a column named Time.

Does this meet the goal?

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HOTSPOT

You have the following code in an Azure Synapse notebook.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the code. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: stacked bar chart

matplotlib.pyplot.bar makes a bar plot.

The bars are positioned at x with the given alignment. Their dimensions are given by height and width. The vertical baseline is bottom (default 0).

Many parameters can take either a single value applying to all bars or a sequence of values, one for each bar.

Stacked bars can be achieved by passing individual bottom values per bar.

Stacked bar chart

This is an example of creating a stacked bar plot with error bars using bar. Note the parameters yeer used for error bars, and bottom to stack the women’s bars on top of the men’s bars.

import matplotlib.pyplot as plt

labels = [‘G1’, ‘G2’, ‘G3’, ‘G4’, ‘G5’]

men_means = [20, 35, 30, 35, 27]

women_means = [25, 32, 34, 20, 25]

men_std = [2, 3, 4, 1, 2]

women_std = [3, 5, 2, 3, 3]

width = 0.35 # the width of the bars: can also be len(x) sequence

fig, ax = plt.subplots()

ax.bar(labels, men_means, width, yerr=men_std, label=’Men’)

ax.bar(labels, women_means, width, yerr=women_std, bottom=men_means, label=’Women’)

ax.set_ylabel(‘Scores’)

ax.set_title(‘Scores by group and gender’)

ax.legend()

plt.show()

Box 2: two items

Blue item and Green Item.

matplotlib.legend

The legend module defines the Legend class, which is responsible for drawing legends associated with axes and/or figures.

Note: A Diagram Legend is an element that you can add to your diagram to provide information about the colors and/or line thicknesses and styles that have been used in the current diagram, where those colors and other styles have some particular meaning.

https://matplotlib.org/stable/gallery/lines_bars_and_markers/bar_stacked.html

https://matplotlib.org/stable/api/legend_api.html

You have an Azure Synapse Analytics dedicated SQL pool and a Microsoft Purview account. The Microsoft Purview account has been granted sufficient permissions to the dedicated SQL pool. You need to ensure that the SQL pool is scanned by Microsoft Purview.

What should you do first?

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You have a Power BI tenant.

You plan to register the tenant in an Azure Purview account.

You need to ensure that you can scan the tenant by using Azure Purview.

Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

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You are using GitHub as a source control solution for an Azure Synapse Studio workspace. You need to modify the source control solution to use an Azure DevOps Git repository.

What should you do first?

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You need to recommend a solution to resolve the query issue of the serverless SQL pool. The solution must minimize impact on the users.

What should you in the recommendation?

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You open a Power Bl Desktop report that contains an imported data model and a single report page. You open Performance analyzer, start recording, and refresh the visuals on the page.

The recording produces the results shown in the following exhibit

What can you identify from the results?

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After you answer a question in this section, 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 open row set 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 using openrowset with to explicitly specify the maximum length for businessName and surveyName.

Does this meet the goal?

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