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Vendor: GitHub

Exam Code: GitHub-Copilot Dumps

Questions and Answers: 104

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NEW QUESTION: 1
You need to evaluate the following code segment:

What happens when the code segment is run?
A. iNum has a value of 2.
B. iNum has a value of 0.
C. iNum has a value of 3.
D. An exception is thrown.
Answer: D

NEW QUESTION: 2

A. Option C
B. Option A
C. Option D
D. Option B
Answer: B
Explanation:
Explanation
With AWS CloudWatch, the user can publish data points for a metric that share not only the same time stamp, but also the same namespace and dimensions. CloudWatch can accept multiple data points in the same PutMetricData call with the same time stamp. The only thing that the user needs to take care of is that the size of a PutMetricData request is limited to 8KB for HTTP GET requests and 40KB for HTTP POST requests.

NEW QUESTION: 3
トレーニングと検証のエラー値に大きな差があるモデルがあります。
新しいモデルを作成し、相互検証を実行する必要があります。
Azure Machine Learning Studioを使用して、新しいモデルのパラメーターセットを識別する必要があります。
各ステップで使用するモジュールはどれですか?答えるには、適切なモジュールを正しい手順にドラッグします。
各モジュールは、1回または複数回使用することも、まったく使用しないこともできます。コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。
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: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample


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