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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q55-Q60):

NEW QUESTION # 55
You have an Azure key vault named KeyVaultl that contains secrets.
You have a Fabric workspace named Workspace!. Workspace! contains a notebook named Notebookl that performs the following tasks:
* Loads stage data to the target tables in a lakehouse
* Triggers the refresh of a semantic model
You plan to add functionality to Notebookl that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID and secret from KeyVaultl to generate the authentication token. Solution: You use the following code segment:
Use notebookutils. credentials.getSecret and specify key vault URL and the name of a linked service.
Does this meet the goal?

A. No
B. Yes

Answer: A

NEW QUESTION # 56
You have a Fabric workspace named Workspace1 that contains an Apache Spark job definition named Job1.
You have an Azure SQL database named Source1 that has public internet access disabled.
You need to ensure that Job1 can access the data in Source1.
What should you create?

A. an integration runtime
B. a data management gateway
C. a managed private endpoint
D. an on-premises data gateway

Answer: C

Explanation:
To allow Job1 in Workspace1 to access an Azure SQL database (Source1) with public internet access disabled, you need to create a managed private endpoint. A managed private endpoint is a secure, private connection that enables services like Fabric (or other Azure services) to access resources such as databases, storage accounts, or other services within a virtual network (VNet) without requiring public internet access. This approach maintains the security and integrity of your data while enabling access to the Azure SQL database.

NEW QUESTION # 57
You need to populate the MAR1 data in the bronze layer.
Which two types of activities should you include in the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Stored procedure
B. WebHook
C. Copy data
D. ForEach

Answer: C,D

Explanation:
MAR1 has seven entities, each accessible via a different API endpoint. A ForEach activity is required to iterate over these endpoints to fetch data from each one. It enables dynamic execution of API calls for each entity.
The Copy data activity is the primary mechanism to extract data from REST APIs and load it into the bronze layer in Delta format. It supports native connectors for REST APIs and Delta, minimizing development effort.

NEW QUESTION # 58
You plan to process the following three datasets by using Fabric:
* Dataset1: This dataset will be added to Fabric and will have a unique primary key between the source and the destination. The unique primary key will be an integer and will start from 1 and have an increment of 1.
* Dataset2: This dataset contains semi-structured data that uses bulk data transfer. The dataset must be handled in one process between the source and the destination. The data transformation process will include the use of custom visuals to understand and work with the dataset in development mode.
* Dataset3. This dataset is in a takehouse. The data will be bulk loaded. The data transformation process will include row-based windowing functions during the loading process.
You need to identify which type of item to use for the datasets. The solution must minimize development effort and use built-in functionality, when possible. What should you identify for each dataset? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 59
You have a Fabric workspace.
You have semi-structured data.
You need to read the data by using T-SQL, KQL, and Apache Spark. The data will only be written by using Spark.
What should you use to store the data?

A. a lakehouse
B. a warehouse
C. a datamart
D. an eventhouse

Answer: A

Explanation:
A lakehouse is the best option for storing semi-structured data when you need to read it using T-SQL, KQL, and Apache Spark. A lakehouse combines the flexibility of a data lake (which can handle semi-structured and unstructured data) with the performance features of a data warehouse. It allows data to be written using Apache Spark and can be queried using different technologies such as T-SQL (for SQL-based querying), KQL (Kusto Query Language for querying), and Apache Spark (for distributed processing). This solution is ideal when dealing with semi-structured data and requiring a versatile querying approach.

NEW QUESTION # 60
......

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