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NEW QUESTION: 1
What describes multiple LUN snapshots?
A. Captures multiple point-in-time data states.
Snapshot data state is marked "unmodified" from its source when attached.
Snapshot tree supports multiple simultaneous attaches.
B. Captures multiple point-in-time data states.
Snapshot data state is marked "modified" from its source when attached.
Snapshot tree supports multiple simultaneous attaches.
C. Captures multiple point-in-time data states.
Snapshot data state is marked "modified" from its source when attached.
Snapshot tree supports only one attach at a time.
D. Captures multiple point-in-time data states.
Snapshot data state is marked "unmodified" from its source when attached.
Snapshot tree supports only one attach at a time.
Answer: C
Explanation:
Explanation/Reference:
Only a single snapshot per LUN can be attached at any given time, and must be detached prior to attaching another snapshot of the same LUN.
NEW QUESTION: 2
You create a virtual machine scale set named Scale1. Scale1 is configured as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
Answer:
Explanation:
Explanation:
As cooling period and scale in and scale out durations are not displayed in the graphical view, so we need to consider the default values as below for these settings.
Cool down (minutes) : The amount of time to wait before the rule is applied again so that the autoscale actions have time to take effect. Default is 5 minutes.
Duration : The amount of time monitored before the metric and threshold values are compared. Default is 10 minutes.
Box 1: 4 virtual machines
The Autoscale scale out rule increases the number of VMs by 2 if the CPU threshold is 80% or higher for more than or equals to 10 mins due to default duration for scale in and out is 10 minutes. Since CPU utilization at 85% only lasts for 6 mins , it does not trigger the rules.
Hence no of virtual machines will be same as the initial value which is 4.
Box 2: 4 virtual machines
The Autoscale scale in rule decreases the number of VMs by 4 if the CPU threshold is 30% or lower for more than or equal to 10 mins. due to default duration for scale in and out is 10 minutes . Since CPU utilization at 30% only lasts for 6 mins , it does not trigger the rules. Hence after first 6 mins instance count will be same as initial count as 4. After that CPU utilization reached to 50% for 6 mins , which again would not trigger the scale in rule. Therefore no of virtual machines will be same as the initial value which is 4.
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
A. Use the TODATETIMEOFFSET function.
B. Use the VARBINARY data type.
C. Use the FORMAT function.
D. Use the DATETIME data type.
E. Use the DATE data type.
F. Use an appropriate collation.
G. Use the DATETIME2 data type.
H. Use the DATETIMEOFFSET data type.
I. Use the CAST function.
J. Use a user-defined table type.
Answer: E
Explanation:
Explanation
The size of a column with the DATE format is 3 bytes fixed.
NEW QUESTION: 4
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment tor fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You plan to configure the resources for part of a workflow that will be used to preprocess data from files stored in Azure Blob storage. You plan to use Python to preprocess and store the data in Hadoop.
You need to get the data into Hadoop as quickly as possible.
Which three actions should you perform? Each correct answer presents pan of the solution.
NOTE: Each correct selection is worth one point.
A. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
B. Create an Azure HDInsight Hadoop cluster.
C. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
D. Process the files by using Python to store the data to a Hadoop instance.
E. Create the Machine Learning experiment, and then add an Execute Python Script module.
Answer: A,C,E
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