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NEW QUESTION: 1
Which command is used to set the eth0 IP address on an SC4020 controller?
A. ifconfig inet eth0 [IP address] [netmask] [gateway]
B. controller ipconfig eth0 [IP address/prefix length]
C. controller ipconfig eth0 [IP address] [netmask] [gateway]
D. ifconfig inet eth0 [IP address/prefix length]
Answer: D

NEW QUESTION: 2
高性能アプリケーション用に運用データベースをホストするAzureソリューションを計画しています。
このソリューションには、次のコンポーネントが含まれています。
* Microsoft SQL Server 2016を実行する2台の仮想マシンは、同じAzureリージョン内の異なるデータセンターに展開され、Always On可用性グループの一部になります。
* SQL Server IaaSエージェント拡張機能の自動バックアップ機能（SQLIaaSExtension）を使用してバックアップされるSQL Serverデータ次の表に示すように、さまざまなデータ型の記憶域の優先順位を識別します。

各データタイプにどのストレージタイプを推奨しますか？答えるには、適切なストレージタイプを正しいデータタイプにドラッグします。各保管域タイプは、1回、複数回、またはまったく使用しないことができます。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要があります。
注：それぞれ正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation

NEW QUESTION: 3
Which of the following is NOT a tool or technique used to perform scope planning?
A. Schedule performance indexing.
B. Expert Judgment.
C. Benefit cost analysis.
D. Alternatives identification.
Answer: A

NEW QUESTION: 4
You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.
You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.
You need to determine the extent to which each feature in the customer data is influencing predictions.
What should you do?
A. Add tags to the model registration indicating the names of the features in the training dataset.
B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.
C. Enable data drift monitoring for the model and its training dataset.
D. Use the interpretability package to generate an explainer for the model.
E. Use the Hyperdrive library to test the model with multiple hyperparameter values.
Answer: D
Explanation:
Explanation
When you compute model explanations and visualize them, you're not limited to an existing model explanation for an automated ML model. You can also get an explanation for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl