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Microsoft AI-102 - Designing and Implementing a Microsoft Azure AI Solution Exam Braindumps

Microsoft AI-102 - Designing and Implementing a Microsoft Azure AI Solution Exam Braindumps

  • Certification Provider:Microsoft
  • Exam Code:AI-102
  • Exam Name:Designing and Implementing a Microsoft Azure AI Solution Exam Exam
  • Total Questions:276 Questions and Answers
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NEW QUESTION: 1
A technician is configuring Multi Link trunking (SMLT) for an Ethernet routing switch (ERS) 8600 switch cluster.
To ensure the fastest possible recovery time, which feature should be avoided?
A. SLPP
B. MLT
C. 802.3 ad
D. VLACP
Answer: D

NEW QUESTION: 2
Roberto, a portal administrator, wants to define the number of communities that are retrieved from the IBM connections server when searching for communities in the Page Associations Windows.
Which property must he change in the WP_ConnectionsIntegrationService resource environment provider?
A. community.picker.page.size
B. community.picker
C. community.picker.communities.number
D. community.picker.number
Answer: A
Explanation:
Explanation/Reference:
Reference: https://www-01.ibm.com/support/knowledgecenter/SSHRKX_8.0.0/admin/ commpages_picker_size.dita

NEW QUESTION: 3
注:この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
次の変数に応じて、学生のアートワークの価格を予測するモデルを作成しています:学生の教育期間、学位タイプ、および芸術形態。
まず、線形回帰モデルを作成します。
線形回帰モデルを評価する必要があります。
解決策:次のメトリックを使用します:平均絶対誤差、ルート平均絶対誤差、相対絶対誤差、相対二乗誤差、および決定係数。
ソリューションは目標を達成していますか?
A. いいえ
B. はい
Answer: B
Explanation:
The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.
Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words: ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
AUC.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION: 4
支払プログラムのカスタマイジングで何を定義しますか?注:この質問には3つの正解があります。
A. 為替レートタイプ
B. 支払実行ID
C. 銀行決定
D. 使用可能な通貨
E. 支払媒体のフォーム
Answer: C,D,E

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