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NEW QUESTION: 1
Given:
Filesystem Size Used Avail Use% Mounted on
/dev/md11 248M 248M 0 100% /
/dev/md13 3.0G 76M 2.8G 3% /config
/dev/md12 1.7G 1.1G 476M 71% /usr
/dev/md14 3.0G 214M 2.6G 8% /var
/dev/md0 30G 2.2G 26G 8% /shared
/dev/md1 6.9G 288M 6.3G 5% /var/log
none 3.9G 452K 3.9G 1% /dev/shm
none 3.9G 19M 3.9G 1% /var/tmstat
none 3.9G 1.2M 3.9G 1% /var/run
prompt 4.0M 12K 4.0M 1% /var/prompt
/dev/md15 12G 8.3G 3.1G 74% /var/lib/mysql
Which command is used to produce this output?
A. du
B. df
C. lsof
D. vmstat
E. ps
Answer: B
NEW QUESTION: 2
HOTSPOT
Answer:
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. 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。
C. 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。
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
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