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| Section | Objectives |
|---|---|
| Implement data engineering solutions | - Lakehouse architecture
|
| Manage and secure Microsoft Fabric | - Governance and administration
|
| Real-time analytics | - Streaming and event processing
|
| Get data into Microsoft Fabric | - Data ingestion and integration
|
| Implement analytics solutions | - Data warehousing and modeling
|
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NEW QUESTION # 101
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1.
You perform the following actions:
Create a workspace named Workspace2.
Create a deployment pipeline named DeployPipeline1 that will deploy items from Workspace1 to Workspace2.
Add a folder named Folder1 to Workspace1.
Move Lakehouse1 to Folder1.
Run DeployPipeline1.
Which structure will Workspace2 have when DeployPipeline1 is complete?
Answer: A
Explanation:
Scenario:
Workspace1 contains: Pipeline1 and Lakehouse1.
A folder named Folder1 is created in Workspace1.
Lakehouse1 is moved into Folder1.
A deployment pipeline DeployPipeline1 deploys from Workspace1 # Workspace2.
Key Behavior:
Deployment pipelines in Microsoft Fabric maintain the folder hierarchy of items when deploying between workspaces.
If an item (Lakehouse1) is placed in a folder (Folder1), the same folder structure is replicated in the target workspace.
Items not placed in the folder (Pipeline1) remain at the root level.
Result in Workspace2 after deployment:
Pipeline1 remains at the root.
Lakehouse1 is placed inside Folder1.
NEW QUESTION # 102
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.
You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The Spark engine will read only the ' SalesOrderNumber ' , ' OrderDate ' , ' CustomerName ' , ' UnitPrice ' columns from Sales_raw.csv. - Yes Removing the partition will reduce the execution time of the query. - No Adding inferSchema= ' true ' to the options will increase the execution time of the query. - Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.
NEW QUESTION # 103
You have a Fabric notebook that has the Python code and output shown in the following exhibit.
Which type of analytics are you performing?
Answer: B
Explanation:
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of "what has happened" by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
NEW QUESTION # 104
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1 and a user named User1.
You need to ensure that User1 can read all the data in Lakehouse1. The solution must follow the principle of least privilege.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
Explanation:
To ensure a user has read access to all data in a Microsoft Fabric lakehouse while following the Principle of Least Privilege (PoLP), you should use Item-level sharing rather than assigning a workspace role.
Workspace roles like Admin, Member, or Contributor grant broad permissions across all items in a workspace, which is often unnecessary for a data consumer.
Implementation Steps
1. Avoid Workspace Roles: Do not add the user as an Admin, Member, or Contributor to the workspace. This prevents them from managing items or writing data.
[D] 2. Use the Share Feature: Navigate to the specific lakehouse and select Share.
3. Configure Granular Permissions: In the sharing dialog, select the following based on the user's technical needs:
3a. Read all SQL endpoint data (ReadData): Required if the user will query data using T-SQL, SQL Server Management Studio (SSMS), or Power BI reports.
[B] 3b. Read all Apache Spark (ReadAll): Required if the user needs to read data via Spark notebooks, OneLake APIs, or the Lakehouse explorer.
3c.Build reports on the default semantic model (Build): Necessary if they need to create new Power BI reports using the lakehouse's data.
Reference:
https://data-mozart.com/understanding-data-access-options-in-microsoft-fabric/
NEW QUESTION # 105
You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.
When you initiate a refresh of Model1, the refresh fails after running out of resources.
What is a possible cause of the failure?
Answer: A
NEW QUESTION # 106
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