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NEW QUESTION: 1
会社には、ITアプリケーションをホストする複数のAWSアカウントがあります。 Amazon CloudWatch Logsエージェントは、すべてのAmazon EC2インスタンスにインストールされます。会社は、ログストレージ専用の集中型AWSアカウントにすべてのセキュリティイベントを集約したいと考えています。
セキュリティ管理者は、複数のAWSアカウント間でイベントをほぼリアルタイムで収集および相関させる必要があります。
これらの要件を満たすソリューションはどれですか？
A. ロギングAWSアカウントのAmazon Kinesis Data Firehoseストリームにデータを発行し、AWS Lambda関数を使用してストリームからメッセージを読み取り、メッセージをData Firehoseにプッシュし、データをAmazon S3に保持するようにCloudWatch Logsエージェントを設定します。
B. CloudWatch Logsを表示する権限を持つ各アプリケーションAWSアカウントにLog Audit IAMロールを作成し、Log Auditロールを引き受けるAWS Lambda関数を設定し、CloudWatch LogsデータのAmazon S3バケットへの1時間ごとのエクスポートを実行しますAWSアカウントのログ。
C. 各アプリケーションAWSアカウントでCloudWatch Logsストリームを設定し、ロギングAWSアカウントのCloudWatch Logsにイベントを転送します。ロギングAWSアカウントで、Amazon Kinesis Data FirehoseストリームをAmazon CloudWatch Eventsにサブスクライブし、ストリームを使用してログデータをAmazon S3に永続化します。
D. ロギングアカウントでAmazon Kinesis Data Streamsを作成し、各アプリケーションAWSアカウントでCloudWatch Logsストリームにストリームをサブスクライブし、データストリームをソースとしてAmazon Kinesis Data Firehose配信ストリームを構成し、ログデータをロギングAWSアカウント内のAmazon S3バケット。
Answer: D
Explanation:
Explanation
The solution uses Amazon Kinesis Data Streams and a log destination to set up an endpoint in the logging account to receive streamed logs and uses Amazon Kinesis Data Firehose to deliver log data to the Amazon Simple Storage Solution (S3) bucket. Application accounts will subscribe to stream all (or part) of their Amazon CloudWatch logs to a defined destination in the logging account via subscription filters.https://aws.amazon.com/blogs/architecture/central-logging-in-multi-account-environments/

NEW QUESTION: 2

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

NEW QUESTION: 3
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Number of hidden layers
B. Weights
C. Biases
D. Number of nodes in each hidden layer
Answer: A,D
Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview