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NEW QUESTION: 1
A ServeRAID adapter is being installed in an IBM xSeries server. Prior to installing a NOS, what utility would be used to
determine the current ServRAID configuration settings?
A. SCSI BIOS Set-Up Utility
B. UpdateXpress CD
C. Configuration/Settings Utility
D. ServRAID Mini-Config
Answer: D
NEW QUESTION: 2
ある会社が新しいモバイルアプリを開発しています。企業は、クロスサイトスクリプティングやSQLインジェクションなどの一般的なアプリケーションレベルの攻撃からアプリケーションロードバラナサー（ALB）を保護するために、適切なトラフィックフィルタリングを実装する必要があります。同社には最小限のインフラストラクチャと運用スタッフがいます。同社は、AWS環境のサーバーの管理、更新、保護における責任の分担を減らす必要があります。
これらの要件を満たすために、ソリューションアーキテクトは何を推奨する必要がありますか？
A. パブリックホスティングを有効にしたAmazonS3を使用してアプリケーションをデプロイします。
B. AWS WAFルールを設定し、それらをALBに関連付けます
C. サードパーティのファイアウォールを実行しているAmazon EC2インスタンスにトラフィックを転送する新しいALBを作成し、サードパーティのファイアウォールがトラフィックを現在のALBに渡します。
D. AWSシールドアドバンストをデプロイし、ALBasaで保護されたリソースを追加します
Answer: C
NEW QUESTION: 3
Which three options must be configured for a new manager to have access to the My Team dynamic object to view and assign training for their employees?
A. Map the new manager to supervisor group.
B. Map employees to the new manager's supervisor account.
C. Create a supervisor account for the new manager.
D. Map a user to a supervisor user role.
E. Create a user account for the new manager.
Answer: A,B,C
NEW QUESTION: 4
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric.
This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?
A. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
B. A scatter plot showing the correlation between maximum tree depth and the objective metric.
C. A histogram showing whether the most important input feature is Gaussian.
D. A scatter plot showing the performance of the objective metric over each training iteration.
Answer: A
Explanation:
https://medium.com/all-things-ai/in-depth-parameter-tuning-for-random-forest-d67bb7e920d