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NEW QUESTION: 1
Which of the following is most likely to be a benefit of concurrent ambulatory surgical case review?
A. Decreased employee turnover
B. An increase in reviewer competence
C. An increase in the number of cases failing screening criteria
D. Decreased medical record review at discharge
Answer: D

NEW QUESTION: 2
Universal Containersのプラットフォーム開発者は、クリックすると一連の計算を実行してユーザーをカスタムVisualforceページにリダイレクトするAccountオブジェクトのカスタムボタンを作成する必要があります。
これを実現するために<apex：page>タグの値で定義する必要がある属性はどれですか。 （3つ選択してください。）
A. アクション
B. 拡張
C. standardController
D. readOnly
E. renderAs
Answer: A,C,E

NEW QUESTION: 3

A. Option A
B. Option C
C. Option D
D. Option B
Answer: C

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.
Does the solution meet the goal?
A. Yes
B. NO
Answer: A
Explanation:
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.
Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data