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NEW QUESTION: 1
ClearPass Onboard uses which type of WLAN security once the user has authenticated and onboarded the client?
A. SSL
B. EAP-PEAP
C. EAP-TLS
D. EAP-LEAP
E. EAP-RADIUS
Answer: C

NEW QUESTION: 2
An administrator for Access Gateway 9.0, Enterprise Edition suspects that some users in the environment are misusing the remote access granted to them by accessing and downloading some restricted intranet web resources.What should the administrator check first on the Access Gateway appliance in order to investigate these users' remote access behavior?
A. Network traces
B. Client side debug trace files
C. Audit logs
D. Statistic counters
Answer: C

NEW QUESTION: 3
SCシリーズアレイストレージ仮想化の正しいシステム階層は何ですか？

Answer:
Explanation:

NEW QUESTION: 4
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 expose a Machine Learning model as an Azure web service.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
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