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NEW QUESTION: 1
What are the limitations of a traditional layer 2 switch? (Choose 3}
A. Slow response to topology changes.
B. Requires layer 2 loop prevention.
C. Limited to 4094 vlans per switch.
D. Isolates collision domains.
E. Increased flooding.
Answer: A,B,C
NEW QUESTION: 2
Marcy is the business analyst for her organization. She is completing the business analysis task of defining the business needs. She has the business goals and objectives and the requirements stated documentation.
What must she confirm about the requirements stated documentation before defining the business need based on her analysis?
A. The requirements must be compatible with the refinement of the business goals and objectives.
B. The requirements must reflect proposed solutions as part of alternative identification.
C. The requirements must reflect actual business requirements, not a description of the solutions.
D. The requirements must contain no or little risks.
Answer: C
NEW QUESTION: 3
The MAC address table on a Cisco Nexus Series switch is full What Layer 2 network behavior will occur while the network MAC address table remains full?
A. The switch will remove all dynamically learned MAC addresses from the table
B. Frames with an unknown destination address will be flooded
C. All frames will be flooded
D. The switch will reset the MAC address table, removing all MAC addresses.
Answer: C
NEW QUESTION: 4
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering
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