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NEW QUESTION: 1
The manager of a call center reports that staffing the center is difficult because the number of calls is unpredictable. You have historical data that contains information about the calls.
You need to build an Azure Machine Learning experiment to predict the number of total calls each hour.
Which model should you use?
A. Multiclass Logistic Regression
B. Boosted Decision Tree Regression
C. Decision Forest Regression
D. Poisson Regression
Answer: D
NEW QUESTION: 2
Answer:
Explanation:
Explanation
Box 1:
The Autoscale scale out rule increases the number of VMs by 2 if the CPU threshold is 80% or higher. The initial instance count is 4 and rises to 6 when the 2 extra instances of VMs are added.
Box 2:
The Autoscale scale in rule decreases the number of VMs by 4 if the CPU threshold is 30% or lower. The initial instance count is 4 and thus cannot be reduced to 0 as the minimum instances is set to 2. Instances are only added when the CPU threshold reaches 80%.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-overview
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-best-practices
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-common-scale-patterns
NEW QUESTION: 3
프로그래머가 프로덕션 프로그램을 악의적으로 수정하여 데이터를 변경한 다음 원래 코드를 복원했습니다. 다음 중 MOST가 악성 활동을 효과적으로 탐지하는 것은 어느 것입니까?
A. 실행 파일 및 소스 코드 무결성 검토
B. 객체 코드 비교
C. 시스템 로그 파일 검토
D. 소스 코드 비교
Answer: C
Explanation:
설명:
프로덕션 라이브러리에서 승인되지 않은 작업에 대한 정보를 제공 할 수있는 유일한 흔적은 시스템 로그 파일을 검토하는 것입니다. 원본 및 개체 코드 비교는 원래 프로그램이 복원되어 존재하지 않기 때문에 효과가 없습니다. 실행 파일과 소스 코드 무결성을 검토하는 것은 비효율적 인 제어입니다. 왜냐하면 실행 파일과 소스 코드 간의 무결성이 자동으로 유지되기 때문입니다.