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NEW QUESTION: 1
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Meta explainers automatically select a suitable direct explainer and generate the best explanation info based on the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated or developed. The following are the meta explainers available in the SDK:
Tabular Explainer: Used with tabular datasets.
Text Explainer: Used with text datasets.
Image Explainer: Used with image datasets.
Box 1: Tabular
Box 2: Text
Box 3: Image
Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298

NEW QUESTION: 2
A user executes the following statements:
REATE TABLE t1 (c1 INT, c2 INT);
CREATE ALIAS a1 FOR t1;
What will be the result if the user executes the following statement?
DROP ALIAS a1;
A. Both T1 and A1 will be dropped
B. The alias A1 will be dropped
C. Nothing, the statement will result in an error
D. The table T1 will be dropped
Answer: B

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:

説明

ボックス1：MICEを使用して交換
MICEを使用して置換：欠損値ごとに、このオプションは新しい値を割り当てます。これは、統計文献で「連鎖方程式を使用した多変量代入」として説明されている方法を使用して計算されます。
「連鎖方程式による多重代入」。多重代入法では、欠損値を埋める前に、データ内の他の変数を使用して、欠損データを持つ各変数が条件付きでモデル化されます。
シナリオ：両方のデータセットのAccessibilityToHighway列に欠損値が含まれています。欠損値を埋める前に、データ内の他の変数を使用して条件付きでモデル化されるように、欠損データを新しいデータに置き換える必要があります。
ボックス2：伝播
すべての欠損値を含む列は、すべての欠損値の列を出力で保持する必要があるかどうかを示します。
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data