Q1.Your organization has three users named User1, User2, and User3.
The following table lists the Fabric workspaces that are provisioned.
There is a security group named Group1 that contains User1 and User3.
A Fabric administrator creates the domains shown in the following table.
User1 then creates a new workspace named Workspace3.
Group1 is also added as the default domain of Domain1.
For each of the following statements, select [Yes] if the statement is true.
Otherwise, select [No].
| Name | Workspace administrator |
|---|---|
| Workspace1 | User1 |
| Workspace2 | User2 |
| Name | Domain administrator |
|---|---|
| Domain1 | User1 |
| Domain2 | User2 |
| Statement | Yes | No | |
|---|---|---|---|
| User3 is granted the Viewer role on Workspace3. | |||
| User3 is granted Domain contributor access on Domain1. | |||
| User2 is granted the Contributor role on Workspace3. |
Show answer
When you add Group1 as the default domain of Domain1, any unassigned workspaces whose administrators are members of Group1 are assigned to Domain1.
Workspace3 is administered by User1, and because User1 belongs to Group1, it qualifies.
However, a domain assignment does not affect workspace roles or item permissions, so User3 is not granted the Viewer role.
On the other hand, User3, as a member of Group1, becomes a domain contributor of Domain1.
User2 is not in Group1 and is not granted the Contributor role on Workspace3 either.
Domains – Microsoft Fabric | Microsoft Learn
Q2.A Fabric eventstream is running that ingests data into a table named Bike_Location in a KQL database.
The table contains the following columns: BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, and Timestamp.
You need to apply transformation and filter logic to make the data ready for use.
The solution must return data for the neighbourhood named Sands End where No_Bikes is 15 or greater.
The results must be sorted in ascending order of No_Bikes.
Solution: You use the following code segment: bike_location | filter Neighbourhood == “Sands End” and No_Bikes >= 15 | sort by No_Bikes | project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp | project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp.
Does this solution meet the goal?
Show answer
The filter condition specifies Sands End and No_Bikes of 15 or greater, and the project also returns the required columns, so those points meet the requirements.
However, with the KQL sort operator, if you omit asc or desc, the default is descending (desc).
Therefore sort by No_Bikes sorts in descending order rather than ascending.
To get ascending order, you must explicitly specify sort by No_Bikes asc.
This code does not meet the ascending-order requirement.
sort operator – Kusto | Microsoft Learn
Q3.You operate five Fabric workspaces.
You use the monitoring hub to monitor the run status of each item.
You need to determine which workspace a specific item is running in.
Which column should you display in the monitoring hub?
Show answer
The activity list in the monitoring hub lets you see the status, start time, and other details of Fabric items that have run.
To find out which workspace a specific item ran in, you refer to the Location column.
Microsoft Learn also describes Location as a filter that indicates the source workspace where the item activity ran.
Start time, capacity, submitted by, and similar columns are not suited to identifying the source workspace.
For the exam, it is important to remember that Location is the column that indicates the workspace.
Monitoring hub: view and track Fabric activities – Microsoft Fabric | Microsoft Learn
Q4.You have a KQL database that contains two tables named Stream and Reference.
Stream stores streaming data in the following format.
Reference stores reference data in the following format.
Both tables contain millions of rows of data.
You are using the following KQL query set: 01 Stream 02 | extend lat = todecimal(GeoLocation.Latitude), long = todecimal(GeoLocation.Longitude) 03 | join kind=inner Reference on DeviceId 04 | project Timestamp, lat, long, Temperature, DeviceName 05 | filter Temperature >= 10 06 | render scatterchart with (kind = map).
You need to reduce the execution time of this KQL query set.
Solution: You change the join type to kind=outer.
Does this solution meet the goal?
| Column name | Data type |
|---|---|
| Timestamp | Datetime |
| GeoLocation | Dynamic |
| Temperature | Decimal |
| DeviceId | Int |
| Column name | Data type |
|---|---|
| DeviceId | Int |
| DeviceName | String |
Show answer
The join types available for the KQL join operator include inner, leftouter, rightouter, and fullouter, but kind=outer is not a valid join type.
In addition, an outer join includes non-matching rows as well, so it typically increases the amount of data processed.
To reduce execution time, it is effective to apply the filter earlier, before the join, to reduce the number of rows processed, and to project only the columns you need.
This change does not meet the performance-improvement requirement.
join operator – Kusto | Microsoft Learn
Q5.You have a Fabric workspace named Workspace1_DEV that contains the following items: 10 reports, 4 notebooks, 3 lakehouses, 2 data pipelines, 2 Dataflow Gen1 dataflows, 3 Dataflow Gen2 dataflows, and 5 semantic models (each configured with a scheduled refresh policy).
To move items from Workspace1_DEV to a new workspace named Workspace1_TEST, you create a deployment pipeline named Pipeline1.
You then deploy all items from Workspace1_DEV to Workspace1_TEST.
For each of the following statements, select [Yes] if the statement is true.
Otherwise, select [No].
| Statement | Yes | No | |
|---|---|---|---|
| The data of the semantic models is deployed to the target stage. | |||
| The Dataflow Gen1 dataflows are deployed to the target stage. | |||
| The scheduled refresh policies are deployed to the target stage. |
Show answer
A Fabric deployment pipeline copies the metadata and definitions of the supported items between stages, but for items such as semantic models, only the metadata is copied, not the data.
Dataflow Gen1 is a legacy item that is currently treated as a Power BI dataflow, but because it is a supported item in deployment pipelines, the Dataflow Gen1 dataflows are deployed.
On the other hand, the scheduled refresh of a semantic model is not copied.
Therefore, in order, the answers are “No, Yes, No”.
The Microsoft Fabric deployment pipelines process – Microsoft Fabric | Microsoft Learn
Q6.A company has a team of developers.
The team is developing a Python library of reusable code used for data transformation.
You create a Fabric workspace named Workspace1 for developing an extract, transform, and load (ETL) solution by using notebooks.
You need to ensure that the library is available by default in new notebooks created in Workspace1.
Which three actions should you perform in sequence?
To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Drag (or tap) the required number of options from the “Options” list on the left into the “Answer area” on the right. You can drag within the answer area to reorder.
- Change the runtime version.
- Install the library.
- Create a pool.
- Create an environment.
- Set the default environment.
- Drag here
Show answer
To use a reusable library as the workspace standard in Fabric notebooks, you first create an environment and add the required Python library to that environment.
An environment is a workspace item that manages the Spark runtime, compute, and libraries together.
Finally, when you set it as the default environment, new notebooks inherit that library configuration as the workspace default.
Simply creating a pool or changing the runtime does not meet the requirement to make the library available by default.
Create, configure, and use an environment in Fabric – Microsoft Fabric | Microsoft Learn
Q7.You have an Azure event hub.
Each event contains the following fields: BikepointID, Street, Neighbourhood, Latitude, Longitude, No_Bikes, and No_Empty_Docks.
You need to ingest these events.
The solution must keep only the events where the Neighbourhood value is Chelsea and store the retained events in a Fabric lakehouse.
What should you use?
Show answer
A Fabric eventstream can ingest events from real-time sources such as Azure Event Hubs, process them, and route them to destinations.
Because the requirement is to keep only the events where Neighbourhood is Chelsea, you use the eventstream’s filter processing.
You can then send the retained events to a lakehouse destination to store them.
A KQL query set is mainly for querying already ingested data, so for this requirement an eventstream is the best fit, more than a streaming dataset or Spark.
Ingest, filter, and transform real-time events and send them to a Microsoft Fabric lakehouse – Microsoft Fabric | Microsoft Learn
Q8.A Fabric eventstream is running that ingests data into a table named Bike_Location in a KQL database.
The table contains the following columns: BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, and Timestamp.
You need to apply transformation and filter logic to make the data ready for use.
The solution must return data for the neighbourhood named Sands End where No_Bikes is 15 or greater.
The results must be sorted in ascending order of No_Bikes.
Solution: You use the following code segment: SELECT BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp FROM bike_location WHERE neighbourhood = ‘Sands End’ AND no_bikes >= 15 ORDER BY no_bikes.
Does this solution meet the goal?
Show answer
The SELECT statement shown has conditions and sorting that are close to the requirements themselves.
However, to run T-SQL in the query editor of a KQL database, you must start with an empty T-SQL comment line (–).
Code without this specifier is interpreted as KQL, so it does not run as a SQL statement.
In a KQL query set, KQL is the primary language, and this solution does not meet the goal.
T-SQL – Kusto | Microsoft Learn
Q9.You are building a data ingestion pattern by using a Fabric data pipeline.
The source is an Azure SQL database that contains 25 tables.
The destination is a lakehouse.
In a warehouse, you create a control table named Control.Object as shown in the exhibit (click the [Exhibit] tab).
You need to build a data pipeline that supports the dynamic ingestion of the tables listed in the control table in a single run.
Which three actions should you perform in sequence?
Drag (or tap) the required number of options from the “Options” list on the left into the “Answer area” on the right. You can drag within the answer area to reorder.
- Add a Get metadata activity to query Control.Object and generate the list of schemas and tables to copy.
- Add an Until activity to iterate over the list of tables and copy the source data to lakehouse Delta tables.
- Add a Lookup activity to query Control.Object and generate the list of schemas and tables to copy.
- Add a ForEach activity to iterate over the list of tables and copy the source data to lakehouse Delta tables.
- Add a Copy data activity as an inner activity of the iterator activity.
- Drag here
Show answer
To dynamically ingest multiple tables from the control table in a single run, you first query Control.Object with a Lookup activity to obtain the list of schemas and tables to copy.
Next, you iterate over that list with a ForEach activity.
Placing a Copy data activity inside each iteration lets you use the current item to copy from the source table to the lakehouse Delta table.
Get metadata is for retrieving metadata of existing objects and is not suited to obtaining the list of rows in a control table.
ForEach activity – Microsoft Fabric | Microsoft Learn
Q10.You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.
In Workspace1, you create a new notebook named Notebook2.
You need to ensure that Notebook2 can be attached to the same Apache Spark session as Notebook1.
What should you do?
Show answer
To attach another notebook to an existing Spark session in Fabric notebooks, you enable high concurrency for notebooks.
In high concurrency mode, compatible notebooks can share the same Spark session.
Enabling dynamic allocation for the Spark pool, changing the runtime version, or increasing the number of executors are settings related to resources or the execution environment and do not directly address the requirement to connect Notebook2 to the same session as Notebook1.
Configure high concurrency mode for notebooks – Microsoft Fabric | Microsoft Learn