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NEW QUESTION: 1
Consider the following code snippet:
If this code were executed, which of the following statements would be true?
A. The lne_job1 job would retain its current priority.
B. The priority of the lne_job1 job would be set to 1.
C. The lne_job1 job would be executed synchronously.
D. The lne_job1 job would run immediately in the users current session.
E. The job will immediately take priority over all running jobs.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
job_priority
This attribute specifies the priority of this job relative to other jobs in the same class as this job. If multiple jobs within a class are scheduled to be executed at the same time, the job priority determines the order in which jobs from that class are picked up for execution by the job coordinator. It can be a value from 1 through 5, with 1 being the first to be picked up for job execution.
If no job priority is specified when creating a job, the default priority of 3 is assigned to it.

NEW QUESTION: 2
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. 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。
C. 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。
D. 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。
E. 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。
Answer: A
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

NEW QUESTION: 3
A storage administrator manually configures a storage switch to use a new static domain ID. After issuing an fcdomain restart vsan 1 command, the administrator sees that the switch is still using the existing domain ID.
What is the reason why the configuration is not working?
A. The disruptive keyword was not used on the command.
B. Domain manager fast restart is not enabled on the switch.
C. Build fabric notifications were not sent to the other switches.
D. Static domain IDs apply only after a switch is rebooted.
Answer: D

NEW QUESTION: 4
BigQueryで重要なクエリを実行する必要がありますが、多くのレコードを返すことが期待されています。クエリの実行にかかる費用を調べたいと考えています。オンデマンド料金を使用しています。あなたは何をするべきか？
A. 選択カウント（*）を実行して、クエリが検索するレコード数を把握します。次に、価格計算ツールを使用して、その行数をドルに変換します。
B. このクエリを定額料金に切り替えてから、オンデマンドに戻します。
C. コマンドラインを使用して予行演習クエリを実行し、返されるバイト数を推定します。次に、料金計算ツールを使用して、そのバイト見積もりをドルに変換します。
D. コマンドラインを使用して予行演習クエリを実行し、読み取られたバイト数を推定します。次に、料金計算ツールを使用して、そのバイト見積もりをドルに変換します。
Answer: D