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Free Microsoft DP-300 Exam Braindumps (page: 7)

Page 7 of 61
PDF with 406 Questions

HOTSPOT (Drag and Drop is not supported)
You configure version control for an Azure Data Factory instance as shown in the following exhibit.


Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.

NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: adf_publish
By default, data factory generates the Resource Manager templates of the published factory and saves them into a branch called adf_publish. To configure a custom publish branch, add a publish_config.json file to the root folder in the collaboration branch. When publishing, ADF reads this file, looks for the field publishBranch, and saves all Resource Manager templates to the specified location. If the branch doesn't exist, data factory will automatically create it. And example of what this file looks like is below:

{
"publishBranch": "factory/adf_publish"
}

Box 2: /dwh_barchlet/ adf_publish/contososales
RepositoryName: Your Azure Repos code repository name. Azure Repos projects contain Git repositories to manage your source code as your project grows. You can create a new repository or use an existing repository that's already in your project.


Reference:

https://docs.microsoft.com/en-us/azure/data-factory/source-control



You plan to build a structured streaming solution in Azure Databricks. The solution will count new events in five-minute intervals and report only events that arrive during the interval.

The output will be sent to a Delta Lake table. Which output mode should you use?

  1. complete
  2. append
  3. update

Answer(s): A

Explanation:

Complete mode: You can use Structured Streaming to replace the entire table with every batch.

Incorrect Answers:
B: By default, streams run in append mode, which adds new records to the table.


Reference:

https://docs.databricks.com/delta/delta-streaming.html



HOTSPOT (Drag and Drop is not supported)
You are performing exploratory analysis of bus fare data in an Azure Data Lake Storage Gen2 account by using an Azure Synapse Analytics serverless SQL pool.

You execute the Transact-SQL query shown in the following exhibit.


Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: CSV files that have file named beginning with "tripdata_2020" Box 2: a header
FIRSTROW = 'first_row'

Specifies the number of the first row to load. The default is 1 and indicates the first row in the specified data file. The row numbers are determined by counting the row terminators. FIRSTROW is 1-based.


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-openrowset



You have a SQL pool in Azure Synapse that contains a table named dbo.Customers. The table contains a column name Email.

You need to prevent nonadministrative users from seeing the full email addresses in the Email column. The users must see values in a format of aXXX@XXXX.com instead.

What should you do?

  1. From the Azure portal, set a mask on the Email column.
  2. From the Azure portal, set a sensitivity classification of Confidential for the Email column.
  3. From Microsoft SQL Server Management Studio, set an email mask on the Email column.
  4. From Microsoft SQL Server Management Studio, grant the SELECT permission to the users for all the columns in the dbo.Customers table except Email.

Answer(s): A

Explanation:

The correct option is A. From the Azure portal, set a mask on the Email column.

By setting a mask on the Email column, you can control the way sensitive data is displayed to non-administrative users. The mask allows you to show a modified version of the data while keeping the original data secure. In this case, you can configure the mask to display the email addresses in the format of aXXX@XXXX.com as required.

Option B, setting a sensitivity classification of Confidential for the Email column, is not the most appropriate choice for this scenario. While sensitivity classifications can be used to label data and apply policies, they do not directly handle the masking of data for display purposes.

Options C and D, setting an email mask from Microsoft SQL Server Management Studio or granting SELECT permission to users for all columns except Email, are not correct because they both involve implementing security measures at the database level, but they do not specifically address the data masking requirement for displaying the email addresses in the desired format.


Reference:

https://learn.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview?view=azuresql



You have an Azure Databricks workspace named workspace1 in the Standard pricing tier. Workspace1 contains an all-purpose cluster named cluster1.

You need to reduce the time it takes for cluster1 to start and scale up. The solution must minimize costs. What should you do first?

  1. Upgrade workspace1 to the Premium pricing tier.
  2. Configure a global init script for workspace1.
  3. Create a pool in workspace1.
  4. Create a cluster policy in workspace1.

Answer(s): C

Explanation:

You can use Databricks Pools to Speed up your Data Pipelines and Scale Clusters Quickly.
Databricks Pools, a managed cache of virtual machine instances that enables clusters to start and scale 4 times faster.


Reference:

https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html






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