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Microsoft DP-600 Exam Questions
- Topic 1: Plan, implement, and manage a solution for data analytics: Planning a data analytics environment, implementing and managing a data analytics environment are discussed in this topic. It also focuses on managing the analytics development lifecycle.
- Topic 2: Prepare and serve data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.
- Topic 3: Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.
- Topic 4: Explore and analyze data: It also deals with performing exploratory analytics. Moreover, the topic delves into query data by using SQL.
Free Microsoft DP-600 Exam Actual Questions
Note: Premium Questions for DP-600 were last updated On Jul. 13, 2024 (see below)
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.
Which three Data view options should you select? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.show()
Does this meet the goal?
The df.show() method also does not meet the goal. It is used to show the contents of the DataFrame, not to compute statistical functions. Reference = The usage of the show() function is documented in the PySpark API documentation.
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.
Which three Data view options should you select? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.
You have a Fabric tenant that contains a semantic model. The model contains 15 tables.
You need to programmatically change each column that ends in the word Key to meet the following requirements:
* Hide the column.
* Set Nullable to False.
* Set Summarize By to None
* Set Available in MDX to False.
* Mark the column as a key column.
What should you use?
Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:
Open the model in Tabular Editor.
Create an Advanced Script using C# to iterate over all tables and their respective columns.
Within the script, check if the column name ends with 'Key'.
For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.
Additionally, mark the column as a key column.
Save the changes and deploy them back to the Fabric tenant.
You have a Microsoft Power Bl semantic model.
You need to identify any surrogate key columns in the model that have the Summarize By property set to a value other than to None. The solution must minimize effort.
What should you use?
To identify surrogate key columns with the 'Summarize By' property set to a value other than 'None,' the Best Practice Analyzer in Tabular Editor is the most efficient tool. The Best Practice Analyzer can analyze the entire model and provide a report on all columns that do not meet a specified best practice, such as having the 'Summarize By' property set correctly for surrogate key columns. Here's how you would proceed:
Open your Power BI model in Tabular Editor.
Go to the Advanced Scripting window.
Write or use an existing script that checks the 'Summarize By' property of each column.
Execute the script to get a report on the surrogate key columns that do not have their 'Summarize By' property set to 'None'.
You can then review and adjust the properties of the columns directly within the Tabular Editor.
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