Free Microsoft DP-700 Exam Dumps Questions

DRAG DROP - (Topic 3)
You are implementing the following data entities in a Fabric environment:
Entity1: Available in a lakehouse and contains data that will be used as a core organization entity
Entity2: Available in a semantic model and contains data that meets organizational standards
Entity3: Available in a Microsoft Power BI report and contains data that is ready for sharing and reuse
Entity4: Available in a Power BI dashboard and contains approved data for executive-level decision making
Your company requires that specific governance processes be implemented for the data. You need to apply endorsement badges to the entities based on each entity??s use case.
Which badge should you apply to each entity? To answer, drag the appropriate badges the correct entities. Each badge may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Solution:


Does this meet the goal?

Correct Answer:A

- (Topic 3)
You have five Fabric workspaces.
You are monitoring the execution of items by using Monitoring hub.
You need to identify in which workspace a specific item runs. Which column should you view in Monitoring hub?

Correct Answer:G
To identify in which workspace a specific item runs in Monitoring hub, you should view the Location column. This column indicates the workspace where the item is executed. Since you have multiple workspaces and need to track the execution of items across them, the Location column will show you the exact workspace associated with each item or job execution.

DRAG DROP - (Topic 3)
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData. The following is a sample of the data in TaxiData.

You need to build two KQL queries. The solution must meet the following requirements: One of the queries must partition RunningTotalAmount by VendorID.
The other query must create a column named FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
How should you complete each query? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Solution:
Partition the RunningTotalAmount by VendorID. - Row_cumsum
The Row_cumsum function computes the cumulative sum of a column while optionally restarting the accumulation based on a condition. In this case, it calculates the cumulative sum of total_amount for each VendorID, restarting when the VendorID changes (VendorID
!= prev(VendorID)).

Create a column FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime, partitioned by payment_type - Row_window_session


Does this meet the goal?

Correct Answer:A

- (Topic 3)
You have a Google Cloud Storage (GCS) container named storage1 that contains the files shown in the following table.

You have a Fabric workspace named Workspace1 that has the cache for shortcuts enabled. Workspace1 contains a lakehouse named Lakehouse1. Lakehouse1 has the shortcuts shown in the following table.

You need to read data from all the shortcuts. Which shortcuts will retrieve data from the cache?

Correct Answer:C
When reading data from shortcuts in Fabric (in this case, from a lakehouse like Lakehouse1), the cache for shortcuts helps by storing the data locally for quick access. The last accessed timestamp and the cache expiration rules determine whether data is fetched from the cache or from the source (Google Cloud Storage, in this case).
Products: The ProductFile.parquet was last accessed 12 hours ago. Since the cache has data available for up to 12 hours, it is likely that this data will be retrieved from the cache, as it hasn't been too long since it was last accessed.
Stores: The StoreFile.json was last accessed 4 hours ago, which is within the cache retention period. Therefore, this data will also be retrieved from the cache.
Trips: The TripsFile.csv was last accessed 48 hours ago. Given that it's outside the typical caching window (assuming the cache has a maximum retention period of around 24 hours), it would not be retrieved from the cache. Instead, it will likely require a fresh read from the source.

- (Topic 3)
You are developing a data pipeline named Pipeline1.
You need to add a Copy data activity that will copy data from a Snowflake data source to a Fabric warehouse.
What should you configure?

Correct Answer:C
When using the Copy data activity in a data pipeline to move data from Snowflake to a Fabric warehouse, the process often involves intermediate staging to handle data efficiently, especially for large datasets or cross-cloud data transfers.
Staging involves temporarily storing data in an intermediate location (e.g., Blob storage or Azure Data Lake) before loading it into the target destination.
For cross-cloud data transfers (e.g., from Snowflake to Fabric), enabling staging ensures data is processed and stored temporarily in an efficient format for transfer.
Staging is especially useful when dealing with large datasets, ensuring the process is optimized and avoids memory limitations.

- (Topic 3)
You have a Fabric workspace that contains an eventstream named EventStreaml. EventStreaml outputs events to a table named Tablel in a lakehouse. The streaming data is souiced from motorway sensors and represents the speed of cars.
You need to add a transformation to EventStream1 to average the car speeds. The speeds must be grouped by non-overlapping and contiguous time intervals of one minute. Each event must belong to exactly one window.
Which windowing function should you use?

Correct Answer:C