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NEW QUESTION: 1
Which unique characteristics of the Data Center Aggregation layer must be considered by an Enterprise Campus designer?
A. Virtualization tools allow a cost effective approach for redundancy in the network design by using two or four VDCs from the same physical switch.
B. Load balancing, firewall services, and other network services are commonly integrated by the use of service modules that are inserted in the aggregation switches.
C. "East-west" server-to-server traffic can travel between aggregation modules by way of the core, but backup and replication traffic typically remains within an aggregation module.
D. Layer 3 routing between the Access and Aggregation layers facilitates the ability to span
VLANs across multiple access switches, which is a requirement for many server virtualization and clustering technologies.
Answer: B

NEW QUESTION: 2
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 creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
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: 3
Which one of these is an example of analytics supporting the Smarter Commerce 'Buy' offerings?
A. Market basket analysis.
B. Customer behavioral segmentation.
C. Social analytics.
D. Linear regression.
Answer: B
Explanation:
Buy: Smarter Commerce optimizes supplier and partner interactions based on changes in shopping/buying behavior across the supply chain and reconsiders partner roles and relationships to generate new and differentiating customer value. IBM's integrated portfolio of solutions includes Consulting & Services, Advanced Analytics & Infrastructure and the following solution offerings: Supplier Integration and Management, Supply Chain Optimization, Logistics Management, Payments and Settlements.
Reference:Smarter Commerce,Redefining commerce in the age of the customer

NEW QUESTION: 4
組織は、ベンダーとの企業間データ共有のためのアプリケーションプログラミングインターフェイス（API）を設計しています。データ漏えいの潜在的なリスクを軽減するための最良の方法は次のうちどれですか？
A. 指定された期間内のAPI呼び出しの許容数を制限します
B. ハイパーテキスト転送プロトコル（HTTP）を介したデータ転送を要求するポリシーを実装します
C. アプリケーションアーキテクチャとサービスレベルアグリーメント（SLA）の独立したレビューを実施する
D. 安全なサーバーにAPIを実装し、両方の組織間のトラフィックを暗号化します
Answer: D