Microsoft DP-300 Practice Test - Questions Answers, Page 7

A shared access signature (SAS) provides secure delegated access to resources in your storage account. With a SAS, you have granular control over how a client can access your data. For example:

What resources the client may access.

What permissions they have to those resources.

How long the SAS is valid.

Note: Data Lake Storage Gen2 supports the following authorization mechanisms:

Shared Key authorization

Shared access signature (SAS) authorization

Role-based access control (Azure RBAC)

Access control lists (ACL) Data Lake Storage Gen2 supports the following authorization mechanisms:

Shared Key authorization

Shared access signature (SAS) authorization

Role-based access control (Azure RBAC)

Access control lists (ACL)

Reference:

https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview

Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics support dynamic data masking. Dynamic data masking limits sensitive data exposure by masking it to non-privileged users. The Credit card masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card. Example:

XXXX-XXXX-XXXX-1234

Transparent data encryption (TDE) helps protect Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics against the threat of malicious offline activity by encrypting data at rest.

Reference:

https://docs.microsoft.com/en-us/azure/azure-sql/database/transparent-data-encryption-tde-overview

Azure RBAC is used to manage who can create, update, or delete the Synapse workspace and its SQL pools, Apache Spark pools, and Integration runtimes. Define and implement network security configurations for resources related to your dedicated SQL pool with Azure Policy.

Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/security/synapse-workspace-synapse-rbac https://docs.microsoft.com/en-us/security/benchmark/azure/baselines/synapse-analytics-security-baseline

A customer-managed key can only be configured on an empty data Factory. The data factory can't contain any resources such as linked services, pipelines and data flows. It is recommended to enable customer-managed key right after factory creation.

Note: Azure Data Factory encrypts data at rest, including entity definitions and any data cached while runs are in progress. By default, data is encrypted with a randomly generated Microsoft-managed key that is uniquely assigned to your data factory.

Incorrect Answers:

A, D: Should enable Soft Delete and Do Not Purge on Azure Key Vault.

Using customer-managed keys with Data Factory requires two properties to be set on the Key Vault, Soft Delete and Do Not Purge. These properties can be enabled using either PowerShell or Azure CLI on a new or existing key vault.

Reference:

https://docs.microsoft.com/en-us/azure/data-factory/enable-customer-managed-key

Box 1: Azure AD authentication

Azure Active Directory authentication supports Multi-Factor authentication through Active Directory Universal Authentication.

Box 2: Contained database users

Azure Active Directory Uses contained database users to authenticate identities at the database level.

Incorrect:

SQL authentication: To connect to dedicated SQL pool (formerly SQL DW), you must provide the following information:

Fully qualified servername

Specify SQL authentication

Username

Password

Default database (optional)

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-authentication

Reference:

https://docs.microsoft.com/en-us/sql/relational-databases/security/contained-database-users-making-your-database-portable?view=sql-server-ver15

Reference:

https://docs.microsoft.com/en-us/azure/azure-sql/database/logins-create-manage

Scenario: The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytics process will perform aggregations that must be done continuously, without gaps, and without overlapping.

Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.

Reference:

https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/stream-analytics/stream-analytics-window-functions.md