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NEW QUESTION: 1
エンタープライズ企業のデータサイエンスチームは、Amazon SageMakerに簡単にアクセスできる安全で費用対効果の高い方法を提供したいと考えています。データサイエンティストはAWSの知識が限られているため、Jupyterノートブックインスタンスを起動できる必要があります。複雑なセットアップ要件を公開することなく、機械学習ストレージボリュームで保存データを暗号化するには、ノートブックインスタンスに事前構成済みのAWS KMSキーが必要です。
企業がデータサイエンティスト向けのセルフサービスメカニズムを設定して、運用オーバーヘッドが最小のAWSアカウントでJupyterノートブックを起動できるようにする方法はどれですか。
A. AWS CloudFormationテンプレートを作成し、事前設定されたKMSキーでAWS :: SageMaker :: Notebooklnstanceリソースタイプを使用してJupyterノートブックインスタンスを起動します。 CloudFormationの[Mappings]セクションを使用して、インスタンス名などのパラメーター名をSmall、Large、X-Largeにマッピングすることにより、パラメーター名を簡略化します。出力セクションを使用してノートブックのURLを表示し、テンプレートをデータサイエンティストのポートフォリオのAWSサービスカタログ製品にアップロードして、データサイエンティストのIAMロールと共有します。
B. データサイエンティストがローカルで実行できるAWS CLIスクリプトを作成します。 AWS CLIスクリプトを実行して事前構成されたKMSキーでJupyterノートブックを起動するときに提供されるパラメーターについて、手順を追って説明します。共有のAmazon S3バケットを使用して、CLIスクリプトをデータサイエンティストに配布します。
C. AWS CloudFormationテンプレートを作成して、AWS :: SaqeMaker :: Notebooklnstanceリソースタイプと事前構成されたKMSキーを使用してJupyterノートブックインスタンスを起動します。 CloudFormationテンプレートにわかりやすい名前を追加します。出力セクションを使用して、ノートブックへのURLを表示します。
共有のAmazon S3バケットを使用して、CloudFormationテンプレートをデータサイエンティストに配布します。
D. 静的Amazon S3ウェブサイトを使用してサーバーレスフロントエンドを作成し、データサイエンティストがフォームに入力してJupyterノートブックインスタンスをリクエストできるようにします。 Amazon API Gatewayを使用してS3ウェブサイトからリクエストを受信し、中央のAWS Lambda関数をトリガーして、データサイエンティスト用に事前構成されたKMSキーでノートブックインスタンスを起動するAmazon SageMakerをAPI呼び出します。次に、フロントエンドWebサイトにコールバックして、ノートブックインスタンスのURLを表示します。
Answer: A
Explanation:
https://aws.amazon.com/blogs/mt/enable-self-service-secured-data-science-using-amazon-sagemaker-notebooks-and-aws-service-catalog/
NEW QUESTION: 2
Which RQL string searches for all EBS volumes that do not have a "DataClassification" tag?
A. config where api.name = 'aws-ec2-describe-volumes, AND json.rule = tags[*]key contains DataClassification
B. config where api.name = ,aws-ec2-describe-volumes' AND json.rule = tags[*]key != DataClassification
C. config where api.name = 'aws-ec2-describe-volumes' AND json.rule = tags[*].key = 1
D. config where api.name = ,aws-ec2-describe-volumes' AND json.rule = tags[*].key exists
Answer: C
NEW QUESTION: 3
次の評価プロパティを持つAzure Migrateプロジェクトがあります。
*ターゲットの場所：米国東部
*ストレージの冗長性：ローカルで冗長
*コンフォートファクター：2.0
*パフォーマンス履歴：1か月
*パーセンタイル使用率：95番目
*価格階層：標準
*オファー：従量制
次の2つの仮想マシンを発見します。
* Windows Server 2016を実行し、20％の使用率で10個のCPUコアを持つVM1という名前の仮想マシン
* Windows Server 2012を実行し、50％の使用率で4つのCPUコアを持つVM2という名前の仮想マシンAzure Migrateは、各仮想マシンにいくつのCPUコアを推奨しますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation
The equation is: 'core usage x comfort factor'. The comfort factor is 2.0.
So VM 1 is 10 cores at 20% utilization which equals 2 cores. Multiply that the comfort factor and you get 4 cores.
VM 2 is 4 cores at 50% utilization which equals 2 cores. Multiply that the comfort factor and you get 4 cores.
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