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NEW QUESTION: 1
You use a Microsoft Azure SQL database as a data warehouse. The database is in the Standard service tier and has 400 elastic database throughput units (eDTUs).
You load data to the database by using Azure Data Factory.
You need to reduce the amount of time it takes to load the dat
a.
Solution: You move the database to a Standard database pool that has 800 eDTUs.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
We need at least 400 eDTUs and the use of a Standard database pool.
References: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dtu-resource-limits

NEW QUESTION: 2
Identify the different type categories in the ABAP dictionary
(More than one answer is correct)
A. ble Types
B. Structures
C. Da Models
D. Da Elements
E. Da definitions
Answer: A,B,D
Explanation:
Topic 2, Volume B

NEW QUESTION: 3
SIMULATION
There were two systems:
system1, main system on which most of the configuration take place

system2, some configuration here

Webserver.
Implement a webserver for the site http://serverX.example.com

Download the webpage from http://station.network0.example.com/pub/rhce/rhce.html

Rename the downloaded file in to index.html

Copy the file into the document root

Do not make any modification with the content of the index.html

Clients within my22ilt.org should NOT access the webserver on your systems

Answer:
Explanation:
Please see explanation
Explanation/Reference:
Explanation:

NEW QUESTION: 4
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.