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NEW QUESTION: 1

A. Option C
B. Option D
C. Option E
D. Option A
E. Option B
Answer: C,D

NEW QUESTION: 2
Click the Exhibit button. In the exhibit, each IP subnet in the network is associated with a unique VLAN ID.
Which action will ensure that Host C will communicate with Host A and Host B?

A. Configure a port-based ACL that permits inter-VLAN routing for all configured VLANs.
B. Configure all switch ports connecting to the host devices as access ports associated with a common VLAN.
C. Configure all switch ports connecting to the host devices as trunk ports associated with all VLANs.
D. Configure an IRB interface for each VLAN and associate it with its corresponding VLAN.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Configuring Routing Between VLANs on One Switch To segment traffic on a LAN into separate broadcast domains, you create separate virtual LANs (VLANs). Of course, you also want to allow these employees to communicate with people and resources in other VLANs. To forward packets between VLANs you normally you need a router that connects the VLANs.
However, you can accomplish this on a Juniper Networks switch without using a router by configuring an integrated routing and bridging (IRB) interface (also known as a routed VLAN interface-- or RVI-- in versions of Junos OS that do not support Enhanced Layer 2 Software).
Reference: http://www.juniper.net/documentation/en_US/junos15.1/topics/example/RVIs-qfx-series- example1.html

NEW QUESTION: 3
You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get("training_data")
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training.data dataset Which property should you set?
A)

B)

C)

D)

A. Option C
B. Option D
C. Option A
D. Option B
Answer: C
Explanation:
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder,
inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb