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NEW QUESTION: 1
ソリューションアーキテクトは、適切なアクセス許可を持つAWSユーザーまたはロールのみが新しいAmazon API Gatewayエンドポイントにアクセスできるようにすることを望んでいます。ソリューションアーキテクトは、リクエストのレイテンシを分析し、サービスマップを作成するために、各リクエストのエンドツーエンドビューが必要ですソリューションアーキテクトはAPI Gatewayアクセス制御を設計し、リクエスト検査を実行できますか？
A. API Gatewayメソッドの場合、AWSJAMに認証を設定します。次に、I AMユーザーまたはロールにREST APIリソースの実行アクセス許可を付与します。エンドポイントにアクセスするときにAPI呼び出し元がAWS署名でリクエストに署名できるようにしますAWS Xを使用します-API Gatewayへのユーザーリクエストを追跡および分析するロイ
B. カスタム認証者がAPIクライアントに呼び出し時にキーとシークレットを渡すように依頼し、Lambdaを使用してIAMシステムに対してキーとシークレットのペアを検証するようにAWS Lambda関数を作成し、AWS X-Rayを使用してトレースとAPI Gatewayへのユーザーリクエストを分析する
C. API Gatewayリソースの場合、CORSを有効に設定し、会社のドメインのみを返しますm Access-Control-Allow-Origin headersその後、IAMユーザーまたはロールにexecute-api REST APIリソースに対するInvoke権限を付与Amazon CloudWatchを使用してトレースするAPI Gatewayへのユーザーリクエストの分析
D. API Gatewayのクライアント証明書を作成するエンドポイントにアクセスする必要があるAWSユーザーとロールに証明書を配布するエンドポイントにアクセスするときにAPI呼び出し元がクライアント証明書を渡すことができるようにするAmazon CloudWatchを使用してAPIへのユーザーリクエストをトレースおよび分析するゲートウェイ。
Answer: D

NEW QUESTION: 2
Which of the following protocols provides maintenance and error reporting function?
A. IGMP
B. ICMP
C. PPP
D. UDP
Answer: B

NEW QUESTION: 3
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values:
* learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the search space for the Hyperdrive experiment.
Which two parameter expressions should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. a normal expression for batch_size
B. a uniform expression for learning_rate
C. a choice expression for learning_rate
D. a uniform expression for batch_size
E. a choice expression for batch_size
Answer: B,E
Explanation:
B: Continuous hyperparameters are specified as a distribution over a continuous range of values. Supported distributions include:
* uniform(low, high) - Returns a value uniformly distributed between low and high D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:
* one or more comma-separated values
* a range object
* any arbitrary list object
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

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