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NEW QUESTION: 1
You administer a Microsoft SQL Server Master Data Services (MDS) model. All model entity members have passed validation.
The current model version should be committed to form a record of master data that can be audited and create a new version to allow the ongoing management of the master data.
You lock the current version. You need to manage the model versions.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area, and arrange them in the correct order.

Answer:
Explanation:

Explanation

Box 1: Validate the current version.
In Master Data Services, validate a version to apply business rules to all members in the model version.
You can validate a version after it has been locked.
Box 2: Commit the current version.
In Master Data Services, commit a version of a model to prevent changes to the model's members and their attributes. Committed versions cannot be unlocked.
Prerequisites:
Box 3: Create a copy of the current version.
In Master Data Services, copy a version of the model to create a new version of it.
Note:
References:

NEW QUESTION: 2
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？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: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION: 3
HOTSPOT


Answer:
Explanation: