Question 1

Which of the following data center deployment scenarios is typically used to achieve business continuity and ensure uninterrupted service to users in the event of a failure?

Options :

A : Intra-city active-active

B : Two places and three centers

C : Edge DC

D : Hybrid cloud

Answer: A,B,D

Question 2

Device configuration parameters can be adjusted when using the edge intelligence function of iMaster NCE-FabricInsight for specified stream analysis. Which of the following device configurations cannot be automatically delivered during UDP specified stream analysis?

Options :

A : Active aging time

B : ACL Number

C : Whether the VXLAN packet is matched

D : Whether to match TAG packets with a layer 1 VLAN

Answer: A

Question 3

Which of the following is wrong in the description of communication between VMs in a DVS distributed virtual switch environment in server virtualization?

Options :

A :

VMs in the same port group, if they are on different hosts, can reach DVS on the destination host through the uplink of DVS and finally complete communication with the remote VM

B :

VMs in different port groups (belonging to different VLANs) need to go through the uplink of the DVS to the physical switch, through which the VLAN is implemented Communications

C : VMs in the same port group, if they are in the same host, can communicate directly via DVS

D :

VMs that are in different DVSs but belong to the same VLAN port group can communicate directly over DVS

Answer: D

Question 4

Which of the following parameters does OpenStack use to define a virtual machine specification?

Options :

A : Label

B : Instance

C : Datasheet

D : Flavor

Answer: D

Question 5

Which of the following descriptions of distributed training is wrong?

Options :

A : In data parallelism, each computing node trains the complete model based on local data

B : Distributed training can support the training of massive data and complex models

C : Data parallelism and model parallelism are common distributed training methods

D :

In model parallelism, each compute node will only interact with the trained sub-model and obtain the final model through model aggregation

Answer: D

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