See the explanation below.
Explanation
1. Launch the Azure portal.
2. Navigate to the settings page of the database db3 that includes the sensitive data you want to mask.
3. Click the Dynamic Data Masking tile that launches the Dynamic Data Masking configuration page.
Note: Alternatively, you can scroll down to the Operations section and click Dynamic Data Masking.

4. In the Dynamic Data Masking configuration page, you may see some database columns that the recommendations engine has flagged for masking.

5. Click ADD MASK for the EmailAddress column
6. Click Save in the data masking rule page to update the set of masking rules in the dynamic data masking policy.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started-portal

See the explanation below.
Explanation
SQL Data Warehouse compute resources can be scaled by increasing or decreasing data warehouse units.
1. Click SQL data warehouses in the left page of the Azure portal.
2. Select datawarehouse from the SQL data warehouses page. The data warehouse opens.
3. Click Scale.

4. In the Scale panel, move the slider left or right to change the DWU setting. Double the DWU setting.

6. Click Save. A confirmation message appears. Click yes to confirm or no to cancel.

Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/quickstart-scale-compute-portal

See the explanation below.
Explanation
1. In the Azure portal, browse to the database db1-copy10543936 that you want to set up for geo-replication.
2. On the SQL database page, select geo-replication, and then select the region to create the secondary database: US West region

3. Select or configure the server and pricing tier for the secondary database.

4. Click Create to add the secondary.
5. The secondary database is created and the seeding process begins.

6. When the seeding process is complete, the secondary database displays its status.

Reference:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-active-geo-replication-portal

Explanation

Scenario: A daily process creates reporting data in REPORTINGDB from the data in SALESDB. The process is implemented as a SQL Server Integration Services (SSIS) package that runs a stored procedure from SALESDB.
Step 1: Create a linked service to each database
Step 2: Create two datasets
You can create two datasets: InputDataset and OutputDataset. These datasets are of type AzureBlob. They refer to the Azure Storage linked service that you created in the previous section.
Step 3: Create a pipeline
You create and validate a pipeline with a copy activity that uses the input and output datasets.
Step 4: Add a copy activity
References:
https://docs.microsoft.com/en-us/azure/data-factory/quickstart-create-data-factory-portal

Explanation/Reference:
Explanation:
Managed instance is a new deployment option of Azure SQL Database, providing near 100% compatibility with the latest SQL Server on-premises (Enterprise Edition) Database Engine, providing a native virtual network (VNet) implementation that addresses common security concerns, and a business model favorable for on-premises SQL Server customers. The managed instance deployment model allows existing SQL Server customers to lift and shift their on-premises applications to the cloud with minimal application and database changes.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-managed-instance