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NEW QUESTION: 1
A university wants to increase the security posture of its network by implementing vulnerability scans of both centrally managed and student/employee laptops. The solution should be able to scale, provide minimum false positives and high accuracy of results, and be centrally managed through an enterprise console. Which of the following scanning topologies is BEST suited for this environment?
A. A combination of cloud-based and server-based scanning engines
B. An active scanning engine installed on the enterprise console
C. A passive scanning engine located at the core of the network infrastructure
D. A combination of server-based and agent-based scanning engines
Answer: B

NEW QUESTION: 2
You need to ensure that emergency road response vehicles are dispatched automatically.
How should you design the processing system? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box1: API App

* Events generated from the IoT data sources are sent to the stream ingestion layer through Azure HDInsight Kafka as a stream of messages. HDInsight Kafka stores streams of data in topics for a configurable of time.
* Kafka consumer, Azure Databricks, picks up the message in real time from the Kafka topic, to process the data based on the business logic and can then send to Serving layer for storage.
* Downstream storage services, like Azure Cosmos DB, Azure SQL Data warehouse, or Azure SQL DB, will then be a data source for presentation and action layer.
* Business analysts can use Microsoft Power BI to analyze warehoused data. Other applications can be built upon the serving layer as well. For example, we can expose APIs based on the service layer data for third party uses.
Box 2: Cosmos DB Change Feed
Change feed support in Azure Cosmos DB works by listening to an Azure Cosmos DB container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.
The change feed in Azure Cosmos DB enables you to build efficient and scalable solutions for each of these patterns, as shown in the following image:

References:
https://docs.microsoft.com/bs-cyrl-ba/azure/architecture/example-scenario/data/realtime-analytics-vehicle-iot?vie

NEW QUESTION: 3
Which of the following configurations requires the use of hierarchical policy maps?
A. the use of class-based WRED within a CBWFQ class queue
B. the use of CBWFQ inside class-based shaping
C. the use of a strict priority-class queue within CBWFQ
D. the use of both the bandwidth and shape statements within a CBWFQ class queue
E. the use of nested class-maps with class-based marking
Answer: B
Explanation:
Class-based weighted fair queuing (CBWFQ) extends the standard WFQ functionality to provide support for user-defined traffic classes. By using CBWFQ, network managers can define traffic classes based on several match criteria, including protocols, access control lists (ACLs), and input interfaces. A FIFO queue is reserved for each class, and traffic belonging to a class is directed to the queue for that class. More than one IP flow, or "conversation", can belong to a class. Once a class has been defined according to its match criteria, the characteristics can be assigned to the class. To characterize a class, assign the bandwidth and maximum packet limit. The bandwidth assigned to a class is the guaranteed bandwidth given to the class during congestion. CBWFQ assigns a weight to each configured class instead of each flow. This weight is proportional to the bandwidth configured for each class. Weight is equal to the interface bandwidth divided by the class bandwidth. Therefore, a class with a higher bandwidth value will have a lower weight. By default, the total amount of bandwidth allocated for all classes must not exceed 75 percent of the available bandwidth on the interface. The other 25 percent is used for control and routing traffic. The queue limit must also be specified for the class. The specification is the maximum number of packets allowed to accumulate in the queue for the class. Packets belonging to a class are subject to the bandwidth and queue limits that are configured for the class.