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NEW QUESTION: 1
Which fabric service provides information about other logged in nodes after receiving a query from a node?
A. Management server
B. Fabric login server
C. Name server
D. Fabric controller
Answer: C
Explanation:
Login types in Switched Fabric
Fabric services define three login types:
Fabric login (FLOGI): Performed between an N_Port and an F_Port. To log on to the
fabric, a node sends a FLOGI frame with the WWNN and WWPN parameters to the login
service at the predefined FC address FFFFFE (Fabric Login Server). In turn, the switch
accepts the login and returns an Accept (ACC) frame with the assigned FC address for the
node. Immediately after the FLOGI, the N_Port registers itself with the local Name Server
on the switch, indicating its WWNN, WWPN, port type, class of service, assigned FC
address and so on. After the N_Port has logged in, it can query the name server database
for information about all other logged in ports.
Port login (PLOGI): Performed between two N_Ports to establish a session. The initiator
N_Port sends a PLOGI request frame to the target N_Port, which accepts it.
The target N_Port returns an ACC to the initiator N_Port. Next, the N_Ports exchange
service parameters relevant to the session.
Process login (PRLI): Also performed between two N_Ports. This login relates to the FC-4
ULPs, such as SCSI. If the ULP is SCSI, N_Ports exchange SCSI-related service
parameters.
EMC E10-001 Student Resource Guide. Module 5: Fibre Channel Storage Area Network
(FC SAN)

NEW QUESTION: 2
What is the ping response to a transmitted echo that needed to be fragmented and fragmentation was not allowed?
A. D
B. ...
C. M
D. U
Answer: C

NEW QUESTION: 3
You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.
You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.
You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.
What should you do?
A. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
B. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
C. Create a local web service deployment configuration and deploy the model to a local Docker container.
D. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
E. Register a new version of the model and update the entry script to load the new version of the model from its registered path.
Answer: A
Explanation:
How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) using Azure Machine Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an Environment object as an input parameter. In this case our service will create a base docker image for you during deployment stage and mount the required models all in one call. The basic deployment tasks are:
1. Register the model in the workspace model registry.
2. Define Inference Configuration:
a. Create an Environment object based on the dependencies you specify in the environment yaml file or use one of our procured environments.
b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.
3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).

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