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NEW QUESTION: 1
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario.
You have a Microsoft SQL Server database that has the tables shown in the Database Diagram exhibit. (Click the Exhibit button.)

You plan to develop a Power BI model as shown in the Power BI Model exhibit. (Click the Exhibit button.)

You plan to use Power BI to import data from 2013 to 2015.
Product Subcategory[Subcategory] contains NULL values.
End of Repeated Scenario.
You implement the Power BI model.
You plan to add a table named Date to the model. The table will have columns for the date, year, month, and end of the last month, and will include data from January 1, 2013 to December 31, 2015.
The Date table and the Sales table will have a relationship.
Which DAX functions should you use to create the columns?
A. CALENDAR, YEAR, MONTH, and ENDOFMONTH
B. CALENDARAUTO, YEAR, MONTH, and EOMONTH
C. CALENDAR, YEAR, MONTH, and EOMONTH
D. CALENDARAUTO, YEAR, MONTH, and ENDOFMONTH
Answer: C
Explanation:
Explanation
References:
https://msdn.microsoft.com/en-us/query-bi/dax/calendar-function-dax
https://msdn.microsoft.com/en-us/query-bi/dax/year-function-dax
https://msdn.microsoft.com/en-us/query-bi/dax/month-function-dax
https://msdn.microsoft.com/en-us/query-bi/dax/eomonth-function-dax

NEW QUESTION: 2
The tasks associated with Landscape transportation are in which workstream?
Please choose the correct answer.
Response:
A. Transition to Operations
B. Technical Architecture and Infrastructure
C. System & Data Migration
D. Application Solution Adoption
Answer: C

NEW QUESTION: 3
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

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