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NEW QUESTION: 1
Which of the following CANNOT be provided by a tool?
A. Data
B. Wisdom
C. Knowledge
D. Information
Answer: B
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 2
DRAG DROP
You plan to create a Docker image that runs an ASP.NET Core application named ContosoApp. You have a setup script named setupScript.psl and a series of application files including ConlosoApp.dll.
You need to create a Dockertile document that meets the following requirements:
* Call setupScript.psl when the container is built.
* Run ContosoApp.dll when the container starts.
The Dockerfile document must be created in the same folder where ContosoApp.dll and setupScript.psl are stored.
Which four commands should you use to develop the solution? To answer, move the appropriate commands from the list of commands to the answer area and arrange them in the correct order.

Answer:
Explanation:

NEW QUESTION: 3
A small electronics company decides to use a contractor to assist with the development of a new FPGA-based device. Several of the development phases will occur off-site at the contractor's labs.
Which of the following is the main concern a security analyst should have with this arrangement?
A. Making multiple trips between development sites increases the chance of physical damage to the FPGAs.
B. Moving the FPGAs between development sites will lessen the time that is available for security testing.
C. FPGA applications are easily cloned, increasing the possibility of intellectual property theft.
D. Development phases occurring at multiple sites may produce change management issues.
Answer: B

NEW QUESTION: 4
Azure ML SDKを使用してバッチ推論パイプラインを作成します。次のコードを使用してパイプラインを実行します。
azureml.pipeline.coreからのインポートパイプライン
azureml.core.experiment import Experimentから
pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])
pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)
パイプライン実行の進行状況を監視する必要があります。
この目標を達成するための2つの可能な方法は何ですか？それぞれの正解は完全なソリューションを示します。
注：それぞれの正しい選択は1ポイントの価値があります。

A. オプションD
B. オプションC
C. オプションB
D. オプションE
E. オプションA
Answer: A,D
Explanation:
Explanation
A batch inference job can take a long time to finish. This example monitors progress by using a Jupyter widget. You can also manage the job's progress by using:
* Azure Machine Learning Studio.
* Console output from the PipelineRun object.
from azureml.widgets import RunDetails
RunDetails(pipeline_run).show()
pipeline_run.wait_for_completion(show_output=True)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step#monitor-the-parallel-run-

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