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NEW QUESTION: 1
Which description of the Cisco Meraki data center design is true?
A. hybrid cloud and on-premises deployment comprised of custom designed servers for real-time replication of data.
B. globally distributed, co-located SSAE16 (SAS70 type II) certified data centers offering 99.99% uptime SLA
C. Regional availability providing data-on demand that replicates customer data and applications across a global infrastructure
D. close partnership with hundreds of third party hosting facilities that integrate with existing controls in on premises environments.
Answer: B

NEW QUESTION: 2
Which of the following documents can you use as a basis for creating freight units?
There are 2 correct answers to this question.
Response:
A. Delivery-based transportation requirement
B. Forwarding order
C. Forwarding quotation
D. Transportation unit
Answer: A,B

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have Azure IoT Edge devices that generate streaming data.
On the devices, you need to detect anomalies in the data by using Azure Machine Learning models.
Once an
anomaly is detected, the devices must add information about the anomaly to the Azure IoT Hub stream.
Solution: You deploy Azure Functions as an IoT Edge module.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring anomalies:
temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly- detection