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NEW QUESTION: 1
カスタマーネットワークでEIGRPがどのように機能しているかを評価するように求められました。
R6で発生しているルートフィルタリングのタイプ
A. プレフィックスリストを使用した配布リスト
B. ACLを使用した配布リスト
C. 距離255を使用するACL
D. ルートマップを使用した配布リスト
Answer: B
NEW QUESTION: 2
You need to implement Role1.
Which command should you run before you create Role1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION: 3
あなたは銀行で働いているデータサイエンティストであり、Azure MLを使用して、顧客がローンを返済する可能性が高いかどうかを予測する機械学習モデルをトレーニングして登録しました。
モデルがどのように選択を行っているかを理解し、モデルが申請者の居住地に基づいてローンを拒否するなどの政府の規制に違反しないことを確認する必要があります。
顧客データの各機能が予測にどの程度影響を与えているかを判断する必要があります。
あなたは何をするべきか？
A. 解釈性パッケージを使用して、モデルの説明を生成します。
B. モデル登録にタグを追加して、トレーニングデータセットの特徴の名前を示します。
C. 既知のラベル値を持ついくつかのテストデータに対してモデルをスコアリングし、その結果を使用して混同行列を計算します。
D. モデルとそのトレーニングデータセットのデータドリフトモニタリングを有効にします。
E. Hyperdriveライブラリを使用して、複数のハイパーパラメーター値でモデルをテストします。
Answer: A
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