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NEW QUESTION: 1
The project board has asked for highlight reports every six weeks during stage 3. As a result, the project manager has asked each team manager for checkpoint reports every six weeks, in order to collate these into the highlight report. The team manager for the 'marketed courses' is new to team management.
Is this an appropriate action as part of the 'controlling a stage' process, and why?
A. Yes, because checkpoint reports are a time-driven control providing progress information.
B. No, because the frequency of reporting should reflect the level of control required for the work package.
C. No, because the project board should decide on the frequency and content of progress reporting.
D. Yes, because the project board will need to be updated with the progress of every team.
Answer: A

NEW QUESTION: 2
A solutions Architect is building a new feature using Lambda to create metadata when a user uploads a picture
to Amazon S3. All metadata must be indexed.
Which AWS service should the architect use to store this metadata?
A. Amazon Kinesis
B. Amazon DynamoDB
C. Amazon S3
D. Amazon EFS
Answer: B

NEW QUESTION: 3
Which two scenarios would cause a Fault Tolerance-enabled virtual machine to fail to power the Secondary virtual machine? (Choose two.)
A. The host has entered a Network Partitioned state.
B. vSphere Distributed Power Management (DPM) is enabled on the host cluster.
C. vSphere High Availability (HA) is disabled on the host cluster.
D. Enhanced vMotion Compatibility (EVC) is enabled on the host cluster.
Answer: A,C

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