DP-300 Exam Dumps : Administering Relational Databases on Microsoft Azure

Basic Concept: This question tests secure database administration, where the control must match the data state, access boundary, identity model, or compliance requirement.

Why B is Correct: a private endpoint matches the expected DP-300 administration action. A private endpoint exposes an Azure service through a private IP address in a virtual network, avoiding public endpoint dependency. The question is not asking for a general Azure capability; it is asking for the feature that produces this result: You need to recommend a solution that will enable remote developers to access DB1 and DB2.

Why A is Wrong: a public endpoint via a database-level firewall rule is a security-related control, but its value depends on whether the requirement is identity, encryption, auditing, network isolation, or data exposure reduction. It does not satisfy the required identity, encryption, firewall, auditing, or data-exposure boundary described in the scenario: You need to recommend a solution that will enable remote developers to access DB1 and DB2.

Why C is Wrong: a public endpoint via a server-level firewall rule is a security-related control, but its value depends on whether the requirement is identity, encryption, auditing, network isolation, or data exposure reduction. It is not the right enforcement point for this case; the scenario needs the control that governs the data or identity path being tested.

Why D is Wrong: a Point-to-Site (P2S) VPN is a security-related control, but its value depends on whether the requirement is identity, encryption, auditing, network isolation, or data exposure reduction. It does not satisfy the required identity, encryption, firewall, auditing, or data-exposure boundary described in the scenario: You need to recommend a solution that will enable remote developers to access DB1 and DB2.

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.