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Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam Practice Test
Question 1
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?
Answer : D
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.
Question 2
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?
Answer : B
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.
Question 3
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.explain()
Does this meet the goal?
Answer : B
The df.explain() method does not meet the goal of evaluating data to calculate statistical functions. It is used to display the physical plan that Spark will execute. Reference = The correct usage of the explain() function can be found in the PySpark documentation.
Question 4
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:
You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.
Which code should you run to populate the results DataFrame?
A)
B)
C)
D)
Answer : A
The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. Reference = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.
Question 5
You need to create a data loading pattern for a Type 1 slowly changing dimension (SCD).
Which two actions should you include in the process? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
Answer : A, D
For a Type 1 SCD, you should include actions that update rows when non-key attributes have changed (A), and insert new records when the natural key is a new value in the table (D). A Type 1 SCD does not track historical data, so you always overwrite the old data with the new data for a given key. Reference = Details on Type 1 slowly changing dimension patterns can be found in data warehousing literature and Microsoft's official documentation.
Question 6
You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?
Answer : D
The correct DMV to identify what is preventing the report query from completing is sys.dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run. Reference = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.
Question 7
You have a Fabric tenant that contains a warehouse.
Several times a day. the performance of all warehouse queries degrades. You suspect that Fabric is throttling the compute used by the warehouse.
What should you use to identify whether throttling is occurring?
Answer : B
To identify whether throttling is occurring, you should use the Monitoring hub (B). This provides a centralized place where you can monitor and manage the health, performance, and reliability of your data estate, and see if the compute resources are being throttled. Reference = The use of the Monitoring hub for performance management and troubleshooting is detailed in the Azure Synapse Analytics documentation.
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