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| Section | Objectives |
|---|---|
| Topic 1: Develop data transformation solutions | - Data modeling and lakehouse transformation
|
| Topic 2: Design and implement data ingestion solutions | - Ingest batch and streaming data into Microsoft Fabric
|
| Topic 3: Manage and monitor data solutions | - Optimize performance and reliability
|
| Topic 4: Secure and govern data in Microsoft Fabric | - Data governance and compliance
|
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NEW QUESTION # 39
You have a Fabric eventstream that ingests events from an Azure Event Hubs source named input1. You compute an aggregate by using a 60-second tumbling window.
You need to ensure that the window meets the following requirements:
* Provides updated results every five seconds.
* Ensures that each update reflects a rolling, 60-second history of events.
What should you do?
Answer: A
Explanation:
A hopping window is the correct window type when an aggregation must cover a fixed historical duration while producing results at a shorter recurring interval. In this case, the required historical range is 60 seconds , while a new calculation must be produced every five seconds . Therefore, configuring a hopping window with a 60-second window size and five-second hop creates overlapping 60-second windows whose start points are five seconds apart. Each output consequently reflects the most recent 60-second period while being recalculated every five seconds.
A tumbling window behaves differently. Tumbling windows divide the event stream into fixed, non- overlapping intervals . A 60-second tumbling window therefore does not provide a rolling result every five seconds. Reducing it to five seconds would increase output frequency but would calculate only five seconds of history.
Microsoft ' s Eventstream SQL documentation demonstrates HoppingWindow specifically for rolling-window scenarios, such as evaluating a five-minute interval every minute. Microsoft Learn Filtering timestamps does not change the aggregation window semantics, and increasing the tumbling duration to 65 seconds does not increase output frequency.
Study Guide reference: Ingest and transform data # Real-Time Intelligence # Eventstreams # windowed aggregation # tumbling and hopping windows.
NEW QUESTION # 40
DRAG DROP
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.
Answer:
Explanation:
Explanation:
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
NEW QUESTION # 41
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:
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.
Answer:
Explanation:
NEW QUESTION # 42
You have a Fabric workspace that contains an eventstream named EventStream1. EventStream1 outputs events to a table in a lakehouse.
You need to remove files that are older than seven days and are no longer in use.
Which command should you run?
Answer: A
Explanation:
VACUUM is used to clean up storage by removing files no longer in use by a Delta table. It removes old and unreferenced files from Delta tables. For example, to remove files older than 7 days:
VACUUM delta.`/path_to_table` RETAIN 7 HOURS;
NEW QUESTION # 43
You have two Fabric workspaces named Workspace1 and Workspace2.
You have a Fabric deployment pipeline named deployPipeline1 that deploys items from Workspace1 to Workspace2. DeployPipeline1 contains all the items in Workspace1.
You recently modified the items in Workspaces1.
The workspaces currently contain the items shown in the following table.
Items in Workspace1 that have the same name as items in Workspace2 are currently paired.
You need to ensure that the items in Workspace1 overwrite the corresponding items in Workspace2. The solution must minimize effort.
What should you do?
Answer: A
Explanation:
When running a deployment pipeline in Fabric, if the items in Workspace1 are paired with the corresponding items in Workspace2 (based on the same name), the deployment pipeline will automatically overwrite the existing items in Workspace2 with the modified items from Workspace1. There's no need to delete, rename, or back up items manually unless you need to keep versions. By simply running deployPipeline1, the pipeline will handle overwriting the existing items in Workspace2 based on the pairing, ensuring the latest version of the items is deployed with minimal effort.
NEW QUESTION # 44
......
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