You have a Fabric workspace that contains a lakehouse named Lakehouse1.You plan to create a data pipeline named Pipeline1 to ingest data into Lakehouse1. You will use a parameter named param1 to pass an external value into Pipeline1. The param1 parameter has a data type of int.You need to ensure that the pipeline expression returns param1 as an int value.How should you specify the parameter value?
Answer(s): B
Parameters for Data Factory in Microsoft FabricExpressions can appear inside strings, using a feature called string interpolation where expressions are wrapped in @{ ... }. For example, the following string includes parameter values and literal string values:"First Name: @{pipeline().parameters.firstName} Last Name: @{pipeline().parameters.lastName}"Using string interpolation, the result is always a string. For example, if you defined myNumber as 42:"@pipeline().parameters.myNumber" Returns 42 as a number.
https://learn.microsoft.com/en-us/fabric/data-factory/parameters
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Workspace1 contains the following items:-A Dataflow Gen2 dataflow that copies data from an on-premises Microsoft SQL Server database to Lakehouse1-A notebook that transforms files and loads the data to Lakehouse1-A data pipeline that loads a CSV file to Lakehouse1You need to develop an orchestration solution in Fabric that will load each item one after the other. The solution must be scheduled to run every 15 minutes.Which type of item should you use?
Answer(s): D
Ingest data by using a data pipeline, dataflow, or notebook Fabric provides three basic ways to retrieve data from existing storage and systems: data pipelines, dataflows, and notebooks. Each item uses a different user approach and targets different types of users.Data pipelines A data pipeline is an item from the Data Factory experience that acts as an orchestration component. It can run other items and services and be scheduled to run at specific times.Incorrect: Not A: Notebooks are items that are used primarily for creating Apache Spark jobs and machine learning experiments.
https://www.microsoftpressstore.com/articles/article.aspx?p=3197446
You are building a Fabric notebook named MasterNotebook1 in a workspace. MasterNotebook1 contains the following code.You need to ensure that the notebooks are executed in the following sequence:1. Notebook_03 2. Notebook_01 3. Notebook_02Which two actions should you perform? Each correct answer presents part of the solution.Note: Each correct selection is worth one point.
Answer(s): D,F
D: Add dependencies to the execution of the last notebook, which is Notebook_02. It depends on Notebook_03 and Notebook_01.Incorrect: Not B: Notebook_03 should be executed first, it does not depend on any other job.Not E: We do not want them to run in parallel. We want to run in a sequence.Note: Reference run multiple notebooks in parallel The method mssparkutils.notebook.runMultiple() allows you to run multiple notebooks in parallel or with a predefined topological structure. The API is using a multi-thread implementation mechanism within a spark session, which means the compute resources are shared by the reference notebook runs.With mssparkutils.notebook.runMultiple(), you can:* Execute multiple notebooks simultaneously, without waiting for each one to finish.*-> Specify the dependencies and order of execution for your notebooks, using a simple JSON format.* Etc. The following is an example of running notebooks with topological structure using mssparkutils.notebook.runMultiple(). Use this method to easily orchestrate notebooks through a code experience.# run multiple notebooks with parameters DAG = { "activities": [ { "name": "NotebookSimple", # activity name, must be unique "path": "NotebookSimple", # notebook path "timeoutPerCellInSeconds": 90, # max timeout for each cell, default to 90 seconds "args": {"p1": "changed value", "p2": 100}, # notebook parameters }, { "name": "NotebookSimple2", "path": "NotebookSimple2", "timeoutPerCellInSeconds": 120, "args": {"p1": "changed value 2", "p2": 200} }, { "name": "NotebookSimple2.2", "path": "NotebookSimple2", "timeoutPerCellInSeconds": 120, "args": {"p1": "changed value 3", "p2": 300}, "retry": 1, "retryIntervalInSeconds": 10, "dependencies": ["NotebookSimple"] # list of activity names that this activity depends on } ], "timeoutInSeconds": 43200, # max timeout for the entire DAG, default to 12 hours "concurrency": 50 # max number of notebooks to run concurrently, default to 50 } mssparkutils.notebook.runMultiple(DAG, {"displayDAGViaGraphviz": False})
https://learn.microsoft.com/en-us/fabric/data-engineering/microsoft-spark-utilities
You have a Fabric workspace that contains a data pipeline named Pipeline1 as shown in the exhibit. (Click the Exhibit tab.)What will occur the next time Pipeline1 runs?
Answer(s): F
HOTSPOT (Drag and Drop is not supported) (Drag and Drop is not supported)You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains a table named Customer. Customer contains the following data.You have an internal Microsoft Entra user named User1 that has an email address of user1@contoso.com.You need to provide User1 with access to the Customer table. The solution must prevent User1 from accessing the CreditCard column.How should you complete the statement? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: SELECT Implement column-level security in Fabric data warehousing Column-level security (CLS) in Microsoft Fabric allows you to control access to columns in a table based on specific grants on these tables.Define column-level access for tables 1. Identify user or roles and the data tables you want to secure with column-level security.2. Implement column-level security with the GRANT T-SQL statement and a column list. For simplicity of management, assigning permissions to roles is preferred to using individuals.-- Grant select to subset of columns of a table GRANT SELECT ON YourSchema.YourTable (Column1, Column2, Column3, Column4, Column5) TO [SomeGroup]; Replace YourSchema with the name of your schema and YourTable with the name of your target table.3. Replace SomeGroup with the name of your User/Group.4. Replace the comma-delimited columns list with the columns you want to give the role access to.5. Repeat these steps to grant specific column access for other tables if needed.Box 2: [User1]
https://learn.microsoft.com/en-us/fabric/data-warehouse/tutorial-column-level-security
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