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NEW QUESTION: 1
Windows Server 2012 R2を実行し、Microsoft SQL Server 2012 R2インスタンスをホストするサーバーが100台あります。
インスタンスは、以下の特性を持つデータベースをホストします。
* 最大のデータベースは現在3 TBです。どのデータベースも4 TBを超えることはありません。
* ストアドプロシージャはCLRを使用して実装されています。
すべてのデータをSQL ServerからAzureに移動する予定です。
データベースをホストするにはAzureサービスを推奨する必要があります。ソリューションは以下の要件を満たす必要があります。
* 可能な限り、移行されたデータベースの管理オーバーヘッドを最小限に抑えます。
* 移行を容易にするために必要なデータベース変更の数を最小限に抑えます。
* ユーザーが自分のActive Directory認証情報を使用して認証できることを確認します。
あなたは推薦に何を含めるべきですか？
A. Azure SQLデータベース管理対象インスタンス
B. Azure仮想マシン上のSQL Server 2016
C. Azure SQL Databaseの単一データベース
D. Azure SQL Databaseエラスティックプール
Answer: A
Explanation:
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-managed-instance
NEW QUESTION: 2
Welcher Befehl generiert automatisch eine IPv6-Adresse aus einem angegebenen IPv6-Präfix und der MAC-Adresse einer Schnittstelle?
A. ipv6 address dhcp
B. ipv6 address 2001:DB8:5:112::2/64 link-local
C. ipv6 address 2001:DB8:5:112::/64 eui-64
D. ipv6 address autoconfig
Answer: D
NEW QUESTION: 3
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:
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
NEW QUESTION: 4
Which number range function would you use to determine if an external number lies in the specified number range interval?
A. NUMBER_RANGE_INTERVAL_LIST
B. NUMBER_CHECK .
C. NUMBER_GET_NEXT
D. NUMBER_GET_INFO
E. NUMBER_RANGE_EXTERNAL_CHECK
Answer: B