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NEW QUESTION: 1
You logged in to your firewall and discovered that the scheduled backup has been modified. Which of the below options is NOT a reason for the change?
A. Another administrator issued a new backup command through the command line
B. Another administrator pushed a SmartProvisioning profile to the firewall
C. Another administrator logged in to the WebUI and changed the setting without your knowledge
D. Another administrator updated the Backup Schedule using SmartUpdate
Answer: D

NEW QUESTION: 2
-- Exhibit -
[edit]
user@switch# commit
error: Trunk interface <ge-0/0/10.0> can not be member of both dot1q-tunneling enabled vlan <cust-1>, and a non dot1q-tunneled vlan <v11> when dot1q-tunneling ethernet-type is not <0x8100>
error: configuration check-out failed
-- Exhibit -
Click the Exhibit button.
When you try to commit your 802.1Q tunneling configuration, you receive the error shown in the exhibit.
Which configuration statement will allow the configuration to commit?
A. set ethernet-switching-options dot1q-tunneling ether-type 0x8100
B. set vlans v11 dot1q-tunneling customer-vlans native
C. set vlans cust-1 interface ge-0/0/10 egress
D. set interfaces ge-0/0/10 ether-options mdi-mode auto
Answer: A

NEW QUESTION: 3
A company with a US-based sales force has requested that the VPN system be configured to authenticate the sales team based on their username, password and a client side certificate.
Additionally, the security administrator has restricted the VPN to only allow authentication from the US territory. How many authentication factors are in use by the VPN system?
A. 0
B. 1
C. 2
D. 3
Answer: C

NEW QUESTION: 4
You need to use the Python language to build a sampling strategy for the global penalty detection models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: import pytorch as deeplearninglib
Box 2: ..DistributedSampler(Sampler)..
DistributedSampler(Sampler):
Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.
Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.
Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10)