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NEW QUESTION: 1
Refer to the exhibit.
interface ethernet 0/0
ip policy route-map PBR
route-map PBR
match ip address 144
set ip next-hop 172.16.12.5
set ip next-hop recursive 192.168.3.2
Which statement correctly describes how a router with this configuration treats packets that matches access-list 144 if it does not know a path to 172.16.12.5, and does not know a path 192.168.3.2?
A. It routes the packet into a loop and drops it when the TTL reaches zero.
B. It send an ICMP source quench message.
C. It drops the packet immediately.
D. It routes the packet based on the packet's destination using the routing table.
Answer: D
NEW QUESTION: 2
You need to meet the Intune requirements for the Windows 10 devices.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/intune/windows-enroll
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