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NEW QUESTION: 1
When analyzing logs for indicators, which are used for only BIOC identification'?
A. techniques
B. artifacts
C. error messages
D. observed activity
Answer: D

NEW QUESTION: 2
Which of the following descriptions about the memory reuse characteristics of FusionCompute is wrong?
A. After the host memory reuse is enabled, the specific memory reuse degree of the virtual machine can be controlled by the virtual machine memory QoS setting
B. After setting the host memory reuse, the total number of virtual machine memory created on the host can exceed the physical memory to increase the density of virtual machines
C. After the host memory reuse is turned off, the sum of the memory sizes of all virtual machines running on each host shall not be greater than the sum of the actual available physical memory used by the virtual machines
D. Turning off host memory reuse requires that the memory reuse rate of all hosts in the cluster is less than or equal to 150%
Answer: D

NEW QUESTION: 3
プロダクションアカウントには、手動でログインしたAmazon EC2インスタンスを24時間以内に終了する必要があるという要件があります。本番アカウントのすべてのアプリケーションは、Amazon CloudWatch Logsエージェントが設定されたAuto Scalingグループを使用しています。
このプロセスはどのように自動化できますか？
A. AWS Lambda関数でCloudWatch Logsサブスクリプションを作成します。ログインイベントを生成したEC2インスタンスにタグを追加し、そのインスタンスを廃止するようにマークする機能を設定します。
このタグですべてのインスタンスを終了させる毎日のLambda関数をトリガーするCloudWatchイベントルールを作成します。
B. ログインイベントでトリガーされるCloudWatchアラームを作成します。オペレーションチームが購読しているAmazon SNSトピックに通知を送信し、24時間以内にEC2インスタンスを終了させます。
C. AWS Step FunctionsアプリケーションへのCloudWatch Logsサブスクリプションを作成します。ログインイベントを生成したEC2インスタンスにタグを追加し、そのインスタンスを廃止するようにマークする機能を設定します。次に、CloudWatchイベントルールを作成して1日に1回、2番目のAWS Lambda関数をトリガーし、このタグですべてのインスタンスを終了させます。
D. ログインイベントでトリガーされるCloudWatchアラームを作成します。 Amazon SQSキューに送信するようにアラームを設定します。ワーカーインスタンスのグループを使用してキューからのメッセージを処理します。これにより、Amazon CloudWatchイベントルールがトリガーされるようにスケジュールされます。
Answer: D

NEW QUESTION: 4
You are developing an application that uses a recommendation engine on Google Cloud. Your solution
should display new videos to customers based on past views. Your solution needs to generate labels for
the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering
suggestions based on data from other customer preferences on several TB of data. What should you do?
A. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.
B. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the
models using Cloud Dataproc. Call the models from your application.
C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user's viewing history to generate
preferences.
D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
Answer: D