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NEW QUESTION: 1
Which two statements are true regarding single row functions?
A. TRUNC: can be used with number and date values
B. INSTR: can be used to find only the first occurrence of a character in a string
C. SYSDATE: returns the database server current date and time
D. MOD: returns the quotient of a division
E. CONCAT: can be used to combine any number of values
F. TRIM: can be used to remove all the occurrences of a character from a string
Answer: A,C
Explanation:
ROUND: Rounds value to a specified decimal TRUNC: Truncates value to a specified decimal MOD: Returns remainder of division SYSDATE is a date function that returns the current database server date and time.
Date-Manipulation Functions
Date functions operate on Oracle dates. All date functions return a value of the DATE data type except MONTHS_BETWEEN, which returns a numeric value. MONTHS_BETWEEN(date1, date2): Finds the number of months between date1 and date2. The
result can be positive or negative. If date1 is later than date2, the result is positive; if date1 is earlier than date2, the result is negative. The noninteger part of the result represents a portion of the month.
ADD_MONTHS(date, n): Adds n number of calendar months to date. The value of n must be an integer and can be negative.
NEXT_DAY(date, 'char'): Finds the date of the next specified day of the week ('char') following date. The value of char may be a number representing a day or a character string.
LAST_DAY(date): Finds the date of the last day of the month that contains date
The above list is a subset of the available date functions. ROUND and TRUNC number functions can also be used to manipulate the date values as shown below:
ROUND(date[, 'fmt']): Returns date rounded to the unit that is specified by the format model fmt. If the format model fmt is omitted, date is rounded to the nearest day.
TRUNC(date[, 'fmt']): Returns date with the time portion of the day truncated to the unit that is specified by the format model fmt. If the format model fmt is omitted, date is truncated to the nearest day.
The CONCAT Function
The CONCAT function joins two character literals, columns, or expressions to yield one larger character expression. Numeric and date literals are implicitly cast as characters when they occur as parameters to the CONCAT function. Numeric or date expressions are evaluated before being converted to strings ready to be concatenated. The CONCAT function takes two parameters. Its syntax is CONCAT(s1, s2), where s1 and s2 represent string literals, character column values, or expressions resulting in character values.
The INSTR(source string, search item, [start position], [nth occurrence of search item]) function returns a number that represents the position in the source string, beginning from the given start position, where the nth occurrence of the search item begins:
instr('http://www.domain.com', '.', 1, 2) = 18
The TRIM function literally trims off leading or trailing (or both) character strings from a given source string:

NEW QUESTION: 2
モデル開発戦略を実装して、ユーザーが広告に反応する傾向を判断する必要があります。
どのテクニックを使用する必要がありますか？
A. 行の分割モジュールを使用して、重心距離に基づいてデータを分割します。
B. 相対式分割モジュールを使用して、イベントまでの移動距離に基づいてデータを分割します。
C. 行の分割モジュールを使用して、イベントまでの移動距離に基づいてデータを分割します。
D. 相対式分割モジュールを使用して、重心距離に基づいてデータを分割します。
Answer: D
Explanation:
Explanation
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

NEW QUESTION: 3

A. WINS
B. DNS
C. DHCP
D. NetBIOS
Answer: C

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