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NEW QUESTION: 1
여러 대륙에 분산 된 팀이 있는 프로젝트 관리자는 일부 팀원의 성과가 감소하고 있음을 발견합니다. 성과 검토 회의에서 이러한 팀 구성원은 회의가 정상 근무 시간 외에 예정되어 있다고 불평합니다. 프로젝트 관리자는 어떻게 해야 합니까?
A. 새로운 협업 기술을 사용하십시오.
B. 커뮤니케이션 관리 계획을 업데이트하십시오.
C. 팀원에게 시간대 차이를 수용하도록 지시하십시오.
D. 라인 매니저와 만나 프로젝트에 더 헌신적인 자원을 찾으십시오.
Answer: B

NEW QUESTION: 2
In an Adaptive Model rule, what is a valid predictor data type?
A. Symbolic
B. Character
C. Boolean
D. String
Answer: B

NEW QUESTION: 3
サービスパックは、以下のソフトウェアです。
A. 以前にリリースされた重要かつ推奨されるすべてのWindows更新プログラムを含みます。
B. オペレーティングシステムの再インストールを含みます。
C. レジストリとファイルシステムを消去します。
D. リモートサービスを有効にします。
Answer: A

NEW QUESTION: 4
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