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NEW QUESTION: 1
監視にカスタムCloudWatchメトリックスが必要なのは次のうちどれですか？
A. EC2インスタンスのメモリ使用率
B. EC2インスタンスのCPU使用率
C. EC2インスタンスのデータ転送
D. EC2インスタンスのディスク使用状況アクティビティ
Answer: A
Explanation:
Explanation
http://docs.aws.amazon.com/AmazonCloudWatch/latest/DeveloperGuide/ec2-metricscollected.html CPU, Disk I/O, Data Transfer are default metrics. Memory is not mentioned.

NEW QUESTION: 2
An access point willbe partially exposed to the outdoor environment in a loading dock of a factory. How should this access point be deployed in a cost-effective manner?
A. Indoor AP with an external leaky coaxial cable
B. Indoor based AP inside a NEMA rated enclosure
C. Outdoor bridge AP pointing its antenna towards the factory
D. Outdoor mesh AP utilizing 5GHz band
Answer: B

NEW QUESTION: 3
Ein Mitarbeiter, der einen Laptop verwendet, der mit dem drahtlosen Netzwerk verbunden ist. Kann eine Verbindung zum Internet herstellen, jedoch nicht zu unternehmensinternen Ressourcen. Ein Systemadministrator überprüft die folgenden Informationen auf dem Laptop:

Der Administrator hat erwartet, dass eine Netzwerkadresse innerhalb von 10.100.0.0/23 angezeigt wird. Dies steht im Einklang mit dem drahtlosen Netzwerk des Unternehmens. Der Netzwerkadministrator überprüft, ob das drahtlose Netzwerk des Unternehmens konfiguriert ist und ordnungsgemäß funktioniert. Welche der folgenden Ursachen ist die wahrscheinlichste Ursache für dieses Problem?
A. Falsche SSID
B. Interferenz
C. Fehlende Übereinstimmung des Sicherheitstyps
D. Kanalüberschneidung
E. Channel overlap
Answer: A

NEW QUESTION: 4
モデルの適合の問題を修正する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation

Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.