DP-500 무료 덤프문제 온라인 액세스

시험코드:DP-500
시험이름:Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI
인증사:Microsoft
무료 덤프 문항수:164
업로드 날짜:2026-07-19
평점
100%

문제 1

You are using a Python notebook in an Apache Spark pool in Azure Synapse Analytics. You need to present the data distribution statistics from a DataFrame in a tabular view. Which method should you invoke on the DataFrame?

문제 2

You have a Power Bl dataset that has the query dependencies shown in the following exhibit.

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

문제 3

You are planning a Power Bl solution for a customer.
The customer will have 200 Power Bl users. The customer identifies the following requirements:
* Ensure that all the users can create paginated reports.
* Ensure that the users can create reports containing Al visuals.
* Provide autoscaling of the CPU resources during heavy usage spikes.
You need to recommend a Power Bl solution for the customer. The solution must minimize costs. What should you recommend?

문제 4

You have an Azure Synapse Analytics workspace that contains an Apache Spark pool. You create a notebook and configure a cell that runs the following SparkSQL query.
SELECT ProductID, ProductName, Category From products.
You need to create a column chart by using the built-in charting capability. The solution must visualize the distribution of product IDs across product categories.

문제 5

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 it. As a result, these questions will not appear in the review screen.
You have the Power Bl data model shown in the exhibit. (Click the Exhibit tab.)

Users indicate that when they build reports from the data model, the reports take a long time to load.
You need to recommend a solution to reduce the load times of the reports.
Solution: You recommend normalizing the data model.
Does this meet the goal?

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