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NEW QUESTION: 1
An LTM Specialist has recently taken over administration or an LTM device that has experienced resource availability issues. The LTM device will need to be solely used for load balancing and SSL offload. Previously, the LTM device was also used to provide statistical analysis of application traffic. However, that functionality has been moved to a third party solution.
Based on the output below, which configuration change should be made to ensure the LTM module receives the most amount of resources?
A. Provision AVR to none. Provision LTM to Dedicated
B. Provision AVR to Minimum. Provision LTM at Maximum
C. Provision LTM to Dedicated, Provision AVR to Dedicated
D. Provision AVR to Minimum, Provision LTM to Dedicated
Answer: A
NEW QUESTION: 2
지점의 IPv6 네트워크 인프라를 구성하고 있습니다.
회사 네트워크 관리자는 지점에서 사용할 2001 : DB8 : 0 : C000 :: / 58 주소 공간을 할당합니다.
만들 수있는 최대 IPv6 서브넷 수를 식별해야 합니다.
몇 개의 IPv6 서브넷을 식별해야 합니까?
A. 0
B. 1
C. 2
D. 3
Answer: D
Explanation:
설명
IPv6에는 128 비트 (16 바이트) 소스 및 대상 IP 주소가 있습니다. 128 비트는 3.4x1038 이상의 가능한 조합을 표현할 수 있지만 IPv6의 큰 주소 공간은 인터넷 백본에서 조직 내 개별 서브넷으로의 여러 수준의 서브넷 및 주소 할당을 위해 설계되었습니다.
참조 : http://technet.microsoft.com/en-us/library/dd379516%28v=WS.10%29.aspx
NEW QUESTION: 3
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.
You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Publish the pipeline.
To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.
Step 2: Retrieve the pipeline ID.
Needed for the schedule.
Step 3: Create a ScheduleRecurrence..
To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.
First create a schedule. Example: Create a Schedule that begins a run every 15 minutes:
recurrence = ScheduleRecurrence(frequency="Minute", interval=15)
Step 4: Define an Azure Machine Learning pipeline schedule..
Example, continued:
recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule",
description="Based on time",
pipeline_id=pipeline_id,
experiment_name=experiment_name,
recurrence=recurrence)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines