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NEW QUESTION: 1
Identify the operation that ensures that referential integrity is maintained while loading changes detected by the ODI CDC framework.
A. Unlock Subscriber
B. Lock Subscriber
C. Extend Window
D. Purge Journal
Answer: C
Explanation:
Extend Window: The Consistency Window is a range of available changes in all the tables of the consistency set for which the insert/update/delete are possible without violating referential integrity. The extend window operation (re)computes this window to take into account new changes captured since the latest Extend Window operation. This operation is implemented using a package step with the Journalizing Model Type. This operation can be scheduled separately from other journalizing operations.
Reference:
http://docs.oracle.com/cd/E21764_01/integrate.1111/e12643/data_capture.htm#CFHIHJEE
NEW QUESTION: 2
The Alpha level of a test (level of significance) represents the yardstick against which P-values are measured and the Null Hypothesis is rejected if the P-value is which of these?
A. Less than the Alpha level.
B. Less than one minus Alpha.
C. Greater than the Alpha level.
D. Greater than the Beta and Alpha level.
E. Less than the power of one minus Beta.
Answer: A
NEW QUESTION: 3
モデルトレーニング要件に合わせて、順列機能の重要度モジュールを構成する必要があります。
あなたは何をするべきか？回答するには、回答領域のダイアログボックス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には1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: 500
For Random seed, type a value to use as seed for randomization. If you specify 0 (the default), a number is generated based on the system clock.
A seed value is optional, but you should provide a value if you want reproducibility across runs of the same experiment.
Here we must replicate the findings.
Box 2: Mean Absolute Error
Scenario: Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
Regression. Choose one of the following: Precision, Recall, Mean Absolute Error , Root Mean Squared Error, Relative Absolute Error, Relative Squared Error, Coefficient of Determination References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importan
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