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NEW QUESTION: 1
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Regression
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Box 2: Classification
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Box 3: Clustering
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION: 2
What is the default time length that Hit Count Data is kept?
A. 4 weeks
B. 6 months
C. 3 month
D. 12 months
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Keep Hit Count data up to - Select one of the time range options. The default is 6 months. Data is kept in the Security Management Server database for this period and is shown in the Hits column.
Reference: http://dl3.checkpoint.com/paid/74/74d596decb6071a4ee642fbdaae7238f/ CP_R80_SecurityManagement_AdminGuide.pdf?
HashKey=1479584563_6f823c8ea1514609148aa4fec5425db2&xtn=.pdf
NEW QUESTION: 3
Webアプリケーションは、単一の顧客Amazon Machine Image（AMI）から起動された10個のEC2インスタンスで実行されます。 EC2インスタンスは、インターネットアプリケーションロードバランサーの背後にあります。 Amazon Route 53は、アプリケーションにDNSを提供します。
Webサーバーインスタンスが要求への応答を停止した場合、Solutions Architectはどのように回復を自動化する必要がありますか？
A. ステータスチェック（任意）が失敗した場合に再起動する各インスタンスのCloudWatchアラームアクションを追加します。
B. 複数のアベイラビリティーゾーンでインスタンスを起動し、ロードバランサーをマルチAZに設定します。
C. Elastic Load BalancingヘルスチェックでAuto Scalingグループのインスタンスを起動します。
D. インスタンスヘルスチェックを使用して、各インスタンスにRoute 53レコードを追加します。
Answer: C
Explanation:
Explanation
https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-add-elb-healthcheck.html
NEW QUESTION: 4
Refer to the exhibits.
Exhibit 1
Exhibit 2
Exhibit 1 shows the topology for the network. The network administrator sees the log entries shown in Exhibit 2. Which type of failure is indicated?
A. A hardware issue caused a unidirectional link; BFD detected the issue at Layer 2 and prevented a broadcast storm.
B. BFD was set up incorrectly on Switch-2, so it caused Switch-2 to lose adjacency with Switch-1 rather than repair the session.
C. A link between Switch-1 and Switch-2 went down. BFD detected the lost connectivity and behaved as expected.
D. Graceful restart helper was not enabled on Switvh-2, so BFD was unable to operate correctly, and the session was taken down.
Answer: B
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