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NEW QUESTION: 1
Which type of SGT propagation does a WLC in a data center require?
A. SGT inline tagging
B. SGT Trusted
C. SXP
D. SGT Reflector
Answer: C

NEW QUESTION: 2
A company needs to configure a new firewall and have only one public IP address to use in this firewall.
The engineer need to configure the firewall with NAT to handle inbound traffic to the mail server in addition to internet outbound traffic. Which options could he use ? (Choose Two)
A. NAT overload for inboud traffic on port 25
B. NAT overload for outbound traffic
C. Dynamic NAT for inbound traffic
D. Dynamic NAT for outbound traffic
E. Static NAT for outbound traffic on port 25
F. Static NAT for inbound traffic on port 25
Answer: B,F

NEW QUESTION: 3
You publish a batch inferencing pipeline that will be used by a business application.
The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.
You need to identify the information required in the REST request and returned as a response from the published pipeline.
Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: JSON containing an OAuth bearer token
Specify your authentication header in the request.
To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header.
Box 2: JSON containing the experiment name
Add a JSON payload object that has the experiment name.
Example:
rest_endpoint = published_pipeline.endpoint
response = requests.post(rest_endpoint,
headers=auth_header,
json={"ExperimentName": "batch_scoring",
"ParameterAssignments": {"process_count_per_node": 6}})
run_id = response.json()["Id"]
Box 3: JSON containing the run ID
Make the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification