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DP-600 Study Tool Make You Master DP-600 Exam in a Short Time

DP-600 Study Tool Make You Master DP-600 Exam in a Short Time
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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q64-Q69):

NEW QUESTION # 64
You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.
You suspect that some DAX queries load unnecessary columns into memory.
You need to identify the frequently used columns that are loaded into memory.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

* A. Query the $system.discovered_STORAGE_TABLE_COLUMN-iN_SEGMeNTS dynamic management view (DMV).
* B. Use the Analyze in Excel feature.
* C. Use the Vertipaq Analyzer tool.
* D. Query the discover_hehory6Rant dynamic management view (DMV).
Answer: A,C

Explanation:
The Vertipaq Analyzer tool (emoticon and querying the
$system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. References = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.

NEW QUESTION # 65
You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?

* A. Get metadata
* B. Stored procedure
* C. Copy data
* D. Lookup
Answer: D

Explanation:
In a Fabric Data Factory pipeline, to execute a stored procedure and make the returned values available for downstream activities, the Lookup activity is used. This activity can retrieve a dataset from a data store and pass it on for further processing. Here's how you would use the Lookup activity in this context:
* Add a Lookup activity to your pipeline.
* Configure the Lookup activity to use the stored procedure by providing the necessary SQL statement or stored procedure name.
* In the settings, specify that the activity should use the stored procedure mode.
* Once the stored procedure executes, the Lookup activity will capture the results and make them available in the pipeline's memory.
* Downstream activities can then reference the output of the Lookup activity.
References: The functionality and use of Lookup activity within Azure Data Factory is documented in Microsoft's official documentation for Azure Data Factory, under the section for pipeline activities.

NEW QUESTION # 66
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following columns.

You create a Fabric notebook and attach it to lakehouse1.
You need to use PySpark code to transform the dat
a. The solution must meet the following requirements:
* Add a column named pickupDate that will contain only the date portion of pickupDateTime.
* Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.


Answer:

Explanation:


NEW QUESTION # 67
You have a Fabric tenant tha1 contains a takehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
EXPLAIN TABLE customer
Does this meet the goal?

* A. No
* B. Yes
Answer: A

NEW QUESTION # 68
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?

* A. No
* B. Yes
Answer: A

Explanation:
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. References = The correct usage of the explain() function can be found in the PySpark documentation.

NEW QUESTION # 69
......

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