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NEW QUESTION: 1
DevOpsエンジニアは、クラシックロードバランサーの背後にあるステートレスRESTfulサービスの状態を追跡する必要があります。新しいアプリケーションリビジョンのデプロイは、Cl / CDパイプラインを介して行われます。サービスの待機時間が定義されたしきい値を超えて増加した場合、サービスが回復するまで展開を停止する必要があります。次の方法のどれが最も速い検出時間を可能にしますか？
A. AWS LambdaおよびElastic Load Balancingアクセスログを使用して、平均レイテンシを検出します。待機時間が定義されたしきい値を超えたときに、警告を発して展開を停止します。
B. Elastic Load Balancingが提供するAmazon CloudWatchメトリックスを使用して、平均レイテンシを計算します。待機時間が定義されたしきい値を超えたときに、警告を発して展開を停止します。
C. AWS CodeDeployの最小健全なホスト設定を使用して、デプロイメントをロールバックするためのしきい値を定義します。これらのしきい値に違反した場合は、デプロイメントをロールバックしてください。
D. メトリックフィルターを使用して、Amazon CloudWatch Logsのアプリケーションログを解析します。待ち時間のフィルターを作成します。待機時間が定義されたしきい値を超えたときに、警告を発して展開を停止します。
Answer: B
Explanation:
https://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-cloudwatch-metrics.html https://docs.aws.amazon.com/codedeploy/latest/userguide/deployments-stop.html

NEW QUESTION: 2
You are designing a solution that will consist of eight HP BladeSystem c7000 enclosures with Virtual Connect FlexFabric. The customer needs to ensure that all management is performed from a single user interface that will allow them to maintain and manage all uplink configurations centrally. How can you demonstrate management of uplinks on all enclosures from a single console?
A. Configure and use Virtual Connect Manager.
B. Deploy and use both HP OneView and Virtual Connect Enterprise Manager.
C. Deploy and use HP OneView.
D. Deploy and use Virtual Connect Enterprise Manager.
Answer: C

NEW QUESTION: 3
You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.
How should you configure the module properties? 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: Mutual Information.
The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.
Box 2: MedianValue
MedianValue is the feature column, , it is the predictor of the dataset.
Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection

NEW QUESTION: 4

A. Option B
B. Option C
C. Option A
D. Option D
E. Option E
Answer: A,C,D