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NEW QUESTION: 1
Linuxノードで2種類のジョブを実行するためにAzureBatchを使用するコスト最適化ソリューションを設計しています。
最初のジョブタイプは、開発環境の短期間のタスクで構成されます。 2番目のjotタイプは、タイムリーなジョブ完了を必要とする実稼働環境向けの長時間実行されるMessage Passing Interface（MPI）アプリケーションで構成されます。
ジョブタイプごとにプールタイプとノードタイプを推奨する必要があります。このソリューションでは、計算料金を最小限に抑え、可能な限りAzure HybridBenefitを活用する必要があります。
あなたは何をお勧めしますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/batch/batch-low-pri-vms

NEW QUESTION: 2
Which commands can be used to create storage groups using a range of devices, device names or a device file?
A. symsg, symdg
B. symaccess, symcfg
C. symdg, symcfg
D. symaccess, symsg
Answer: D

NEW QUESTION: 3
For monitoring space growth over time, which statistics should NOT be considered?
A. SYSIBM.SYSTABLESPACE
DSSIZE, AVGROWLEN
SYSIBM.SYSINDEXES
PIECESIZE, AVGKEYLEN
B. SYSIBM.SYSTABLESPACESTATS
SPACE
SYSIBM.SYSINDEXSPACESTATS
SPACE
C. SYSIBM.SYSTABLEPART_HIST
CARDF, SPACEF
SYSIBM.SYSINDEXPART_HIST
CARDF, SPACEF
D. SYSIBM.SYSTABLEPART
CARDF, SPACEF
SYSIBM.SYSINDEXPART
CARDF, SPACEF
Answer: A

NEW QUESTION: 4
Azure Machine Learningデザイナーを使用して実験を構築しています。
データセットをトレーニングセットとテストセットに分割します。 Two-Class Boosted Decision Treeをアルゴリズムとして選択します。
モデルの曲線下面積（AUC）を決定する必要があります。
どの3つのモジュールを順番に使用する必要がありますか？回答するには、適切なモジュールをモジュールのリストから回答領域に移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation:
Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:

Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.