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NEW QUESTION: 1
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION: 2
Which three ranges are used as private IP addresses? (Choose three.)
A. 172.16.0.0 to 172.32.0.0
B. 172.160.0 to 172.31.255.255
C. 10.0.0.0 to 10.255.255.255
D. 10.0.0.0 to 11.255.255.254
E. 192.168.0.0 to 192.168.255.255
Answer: B,C,E
NEW QUESTION: 3
Which statement is NOT correct?
A. Partitioning files can assist parallel processing.
B. Partitions may be distributed across multiple systems.
C. Partitioning files can improve performance.
D. Only files larger than 64 MB can he partitioned.
Answer: D
NEW QUESTION: 4
企業は、既存のハイパフォーマンスコンピューティング（HPE）ソリューションをAWSクラウドに移行することを計画しています。
既存のソリューションは、単一のラックで開発された高速相互接続性を備えたLinuxを実行する12ノードのクラスターで構成されています。ソリューションアーキテクトは、HPEクラスターのパフォーマンスを最適化する必要があります。
これらの要件を満たすステップの組み合わせはどれですか？ （2つ選択してください。）
A. Elastic Fabric Adapter（EFA）をサポートするAmazon EC2インスタンスを使用します
B. 少なくとも3つのアベイラビリティーゾーンにインスタンスをデプロイします
C. バースト可能なパフォーマンスをサポートするAmazon EC2インスタンスを使用します
D. CPUハイパーヘッドを有効にする
E. Amazon EC2インスタンスをプレースメントグループにデプロイする
Answer: D,E