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NEW QUESTION: 1
これらのグローバルステータス変数を検討してください。
出力から2つの結論を出すことができますか？
A. クエリを実行するよりも多くのアイドル状態の接続があります。
B. thread_cache_sizeを6以上に設定してスレッドキャッシュを設定しました。
C. 出力時にMySQLへの510の接続があります。
D. 出力時に140個のPerformance Schemaスレッドがあります。
E. すべてのmax_connectionsが2018-03-22 14:54:06に使用されていました
Answer: A,C
NEW QUESTION: 2
Answer:
Explanation:
Explanation
Step 1: New-AzureRmNetworkSecurityRuleConfig
Step 2: New-AzureRmNetworkSecurityGroup
Step 3: New-AzureRmVirtualNetworkSubnetConfig
Step 4: New-AzureRmVirtualNetwork
Example: Create a virtual network with a subnet referencing a network security group New-AzureRmResourceGroup -Name TestResourceGroup -Location centralus
$rdpRule = New-AzureRmNetworkSecurityRuleConfig -Name rdp-rule -Description "Allow RDP" -Access Allow -Protocol Tcp -Direction Inbound -Priority 100 -SourceAddressPrefix Internet -SourcePortRange *
-DestinationAddressPrefix * -DestinationPortRange 3389
$networkSecurityGroup = New-AzureRmNetworkSecurityGroup -ResourceGroupName TestResourceGroup
-Location centralus -Name "NSG-FrontEnd" -SecurityRules $rdpRule
$frontendSubnet = New-AzureRmVirtualNetworkSubnetConfig -Name frontendSubnet -AddressPrefix
"10.0.1.0/24" -NetworkSecurityGroup $networkSecurityGroup
$backendSubnet = New-AzureRmVirtualNetworkSubnetConfig -Name backendSubnet -AddressPrefix
"10.0.2.0/24" -NetworkSecurityGroup $networkSecurityGroup
New-AzureRmVirtualNetwork -Name MyVirtualNetwork -ResourceGroupName TestResourceGroup
-Location centralus -AddressPrefix "10.0.0.0/16" -Subnet $frontendSubnet,$backendSubnet References:
https://docs.microsoft.com/en-us/powershell/module/azurerm.network/new-azurermvirtualnetwork?view=azurer
NEW QUESTION: 3
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
NEW QUESTION: 4
Sie haben ein Azure-Abonnement, das einen Azure-Dienstbus mit dem Namen Bus1 enthält.
Ihr Unternehmen plant, zwei Azure-Webanwendungen mit den Namen App1 und App2 bereitzustellen. Die Web-Apps erstellen Nachrichten mit den folgenden Anforderungen:
* Jede von App1 erstellte Nachricht darf nur von einem einzelnen Verbraucher konsumiert werden
* Jede von App2 erstellte Nachricht wird von mehreren Verbrauchern konsumiert.
Welche Ressource sollten Sie für jede Web-App erstellen? Ziehen Sie zum Beantworten die entsprechenden Ressourcen auf die richtigen Webanwendungen. Jede Ressource kann einmal, mehrmals oder gar nicht verwendet werden. Möglicherweise müssen Sie die geteilte Leiste zwischen den Bereichen ziehen oder einen Bildlauf durchführen, um den Inhalt anzuzeigen.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Answer:
Explanation:
Explanation
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