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NEW QUESTION: 1

A. Option E
B. Option A
C. Option C
D. Option D
E. Option B
Answer: A,B
Explanation:
Explanation
Unlike unusable indexes, an invisible index is maintained during DML statements.
Note:
* Oracle 11g allows indexes to be marked as invisible. Invisible indexes are maintained like any other index, but they are ignored by the optimizer unless the OPTIMIZER_USE_INVISIBLE_INDEXES parameter is set to TRUE at the instance or session level. Indexes can be created as invisible by using the INVISIBLE keyword, and their visibility can be toggled using the ALTER INDEX command.

NEW QUESTION: 2
You have a hybrid environment that includes Microsoft Azure AD. On-premises applications use Active Directory Domain Services (AD DS) for authentication.
You need to determine which authentication methods to use.
Match each feature to its authentication source. To answer, drag the appropriate authentication sources from the column on the left to the client features on the right. Each authentication source may be used once, more than once, or not at al.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION: 3
AccessibilityToHighway列の欠落データを置き換える必要があります。
Clean Missing Dataモジュールをどのように構成する必要がありますか？回答するには、回答エリア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: Replace using MICE
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Box 2: Propagate
Cols with all missing values indicate if columns of all missing values should be preserved in the output.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

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