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NEW QUESTION: 1
GREトンネルの2つの主要なコンポーネントは何ですか？ （2つ選択してください。）
A. Ethernet header
B. IP header
C. payload packet
D. LLC header
E. GRE header
Answer: C,E

NEW QUESTION: 2
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is the same in each question in this series.
Start of repeated scenario
You have six workbook queries that each extracts a table from a Microsoft Azure SQL database. The table are loaded to the data model, but the data is not loaded to any worksheets. The data model is shown in the Data Model exhibit. (Click the Exhibit button.) Your company has 100 product subcategories and more than 10,000 products.

End of repeated scenario.
You have a PivotChart that uses Manufacturer as the axis and the sum of SalesAmount as the values.
You need to ensure that only the top 10 manufactures appear in the chart.
What should you do?
A. Configure the Value Filters.
B. Create a calculated column.
C. Change the format of the SalesAmount field.
D. Summarize the SaleAmount field by Max.
Answer: B

NEW QUESTION: 3
You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.
You must use MLflow as the logging package for tracking metrics generated in the experiment.
You need to complete the script for the experiment.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: import mlflow
Import the mlflow and Workspace classes to access MLflow's tracking URI and configure your workspace.
Box 2: mlflow.start_run()
Set the MLflow experiment name with set_experiment() and start your training run with start_run().
Box 3: mlflow.log_metric(' ..')
Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.
Box 4: mlflow.end_run()
Close the run:
run.endRun()
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow