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NEW QUESTION: 1
What is the installation directory restriction when integrating IBM Tivoli Network Manager IP Edition V3.9 with IBM Tivoli Business Service Manager?
A. The directory must not contain mixed case.
B. The directory must not contain periods.
C. The directory must not contain slashes.
D. The directory must not contain spaces.
Answer: D

NEW QUESTION: 2

A. Option A
B. Option B
C. Option C
D. Option E
E. Option D
Answer: D,E
Explanation:
Use a trigger that starts a job which executes the application.
References: http://technet.microsoft.com/en-us/library/hh849759.aspx

NEW QUESTION: 3





A. Option A
B. Option B
Answer: B
Explanation:
We should use a WHERE clause, not a HAVING clause. The HAVING clause would refer to aggregate data.

NEW QUESTION: 4
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. 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。
B. 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。
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. 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。
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: D
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.
Incorrect Answers:
A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons where model accuracy degrades over time, thus monitoring data drift helps detect model performance issues.
B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model.
C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl