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NEW QUESTION: 1
Your database contains tables named Products and ProductsPriceLog. The Products table contains columns named ProductCode and Price. The ProductsPriceLog table contains columns named ProductCode, OldPrice, and NewPrice.
The ProductsPriceLog table stores the previous price in the OldPrice column and the new price in the NewPrice column.
You need to increase the values in the Price column of all products in the Products table by 5 percent. You also need to log the changes to the ProductsPriceLog table.
Which Transact-SQL query should you use?
A. UPDATE Products SET Price = Price * 1.05
OUTPUT inserted.ProductCode, deleted.Price, inserted.Price
INTO ProductsPriceLog(ProductCode, OldPrice, NewPrice)
B. UPDATE Products SET Price = Price * 1.05
OUTPUT inserted.ProductCode, inserted.Price, deleted.Price
INTO ProductsPriceLog(ProductCode, OldPrice, NewPrice)
C. UPDATE Products SET Price = Price * 1.05
INSERT INTO ProductsPriceLog(ProductCode, OldPrice, NewPrice)
SELECT ProductCode, Price, Price * 1.05 FROM Products
D. UPDATE Products SET Price = Price * 1.05
OUTPUT inserted.ProductCode, deleted.Price, inserted.Price * 1.05
INTO ProductsPriceLog(ProductCode, OldPrice, NewPrice)
Answer: A
Explanation:
Explanation/Reference:
Reference: http://msdn.microsoft.com/en-us/library/ms177564.aspx

NEW QUESTION: 2
Das folgende Diagramm ist ein Beispiel für:

A. Projektteam.
B. Projektstrukturplan (PSP).
C. Risikostrukturplan (RBS).
D. SWOT-Analyse.
Answer: C

NEW QUESTION: 3
An end user reports that the video they are receiving from a CTS-500 is showing a heavy blue tint. The room sending the video uses indoor artificial lighting. Which procedure must be performed to resolve the issue?
A. camera focus balance calibration
B. camera auto adjust room environment
C. camera temperature map adjustment
D. camera brightness mode: indoor
E. camera auto adjustment
Answer: E

NEW QUESTION: 4
次の形式のsalesDataという名前のPythonデータフレームがあります。

データフレームは、次のように長いデータ形式にアンピボットする必要があります。

変換を実行するには、Pythonでpandas.melt（）関数を使用する必要があります。
コードセグメントをどのように完成させる必要がありますか？回答するには、回答領域で適切なオプションを選択します。
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: dataFrame
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source] Where frame is a DataFrame Box 2: shop Paramter id_vars id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables.
Box 3: ['2017','2018']
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
Example:
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
References:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

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