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NEW QUESTION: 1
You are responsible for the company network and provide technical support for your company. You are in charge of offering service for a customer named Alex. There are 200 computers in the network of the customer. All purchasing information is maintained by the Sales department. All licenses are purchased by Alex under the Microsoft Open License program.In order to compare the licenses and the current hardware and software inventory, you have to collect information from the customer. Of the following tools, which should be used?
A. EOpen and Microsoft System Center Operations Manager (OpsMgr) should be used.
B. EOpen and Microsoft System Center Configuration Manager (CfgMgr) should be used.
C. Microsoft Volume Licensing Services (MVLS) and Microsoft System Center Configuration Manager (CfgMgr) should be used.
D. Microsoft Volume Licensing Services (MVLS) and Microsoft System Center Operations Manager (OpsMgr) should be used.
Answer: B

NEW QUESTION: 2
Scenario: A Citrix Administrator needs to set up a XenDesktop environment. The administrator chooses to install the SQL Server version that is included on the XenDesktop installation media.
By choosing this option, which edition of SQL Server license is installed for the XenDesktop environment?
A. SQL Server Developer Edition
B. SQL Server Standard Edition
C. SQL Server Enterprise Edition
D. SQL Server Express Edition
Answer: D

NEW QUESTION: 3
さまざまな都市の家の所有権に関する人口統計データを探索する予定です。データは、次の形式のCSVファイルです。
年齢、都市、収入、自宅所有者
21,Chicago,50000,0
35,Seattle,120000,1
23,Seattle,65000,0
45,Seattle,130000,1
18,Chicago,48000,0
データを探索して結果を記録するには、Azure Machine Learningワークスペースで実験を実行する必要があります。実験では、次の情報を記録する必要があります。
*データセット内の観測数
* home_ownerによる収入の箱ひげ図
*都市名と各都市の平均収入を含む辞書必要な情報をログに記録するには、実験の実行オブジェクトの適切なロギングメソッドを使用する必要があります。
どのようにコードを完成させる必要がありますか？回答するには、適切なコードセグメントを正しい場所にドラッグします。各コードセグメントは、1回、複数回、またはまったく使用しない場合があります。コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。
注：それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: log
The number of observations in the dataset.
run.log(name, value, description='')
Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Box 2: log_image
A box plot of income by home_owner.
log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.
Example: run.log_image("ROC", plot=plt)
Box 3: log_table
A dictionary containing the city names and the average income for each city.
log_table: Log a dictionary object to the run with the given name.