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NEW QUESTION: 1
データアナリストは、会社の6つの異なる部門に対して、同じ要素のメトリックとディメンションを使用してダッシュボードを作成する必要があります。部門は「会社部門」と呼ばれる変数によってキャプチャされます。すべてのユーザーはすべてのデータにアクセスできる必要があります。
分析ワークスペースでこのタスクを実行する正しい方法は何ですか？
A. プロジェクトのコピーオプションを使用し、名前を変更して、すべて同じ要素を保持し、適切なグループと共有します。
B. パネルを自動的に通過するようにAdobeWorkspaceを構成します。
C. アナリストワークスペースでダッシュボードの視覚化を使用する
D. ドロップダウンオプションを使用して、ユーザーがダッシュボードを変更できるように、分割を定義するディメンションを変更できるようにします。
Answer: A
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 create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
The two steps are present: process_step and train_step
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py
NEW QUESTION: 3
What are the Use Enterprise Search with MDG provides the following benefits ?
A. Free-text search across many attributes.
B. Attribute-based search in specific fields only.
C. Fuzzy search checkbox to enable error-tolerant search mode.
D. Search active data only to limit search to active area.
E. MDG running on SAP HANA
Answer: A,B,C,D
NEW QUESTION: 4
複数のアベイラビリティーゾーン（AZ）でAmazon EC2で実行されている会社の新しいウェブアプリケーションは、通常の営業時間中に頻繁にアクセスされます。営業時間後、使用量は最小限になります。
トラフィック需要を処理するためにEC2フリートのサイズを決定するには、どのフリートスケーリングアプローチを使用する必要がありますか？
A. ピークトラフィックのプロビジョニング
B. すべてのAZにわたる手動スケーリング
C. オフピーク時間中の1つのAZのすべてのインスタンスのプログラムによる終了
D. スケジュールされたスケーリング
Answer: D
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