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NEW QUESTION: 1
User response time is the sum of which four metrics?
A. Server response time, data transfer time, connection setup time, application turn rate
B. Inbound round trip time, server response time, retransmission delay, payload transfer time
C. Inbound round trip time, outbound round trip time, server response time, payload transfer time
D. Connection setup time, server response time, payload transfer time, retransmission delay
Answer: D
NEW QUESTION: 2
Cisco APICコントローラーと従来のSDNコントローラーについて正しい説明はどれですか。
A. APICは命令モデルを使用します
B. APICはサウスバウンドRESTAPIをサポートしています
C. APICは、ポリシーエージェントを使用してポリシーを次のように変換します。
D. APICはノースバウンドプロトコルとしてOpFlexをサポートしています
Answer: C
Explanation:
Explanation
The southbound protocol used by APIC is OpFlex that is pushed by Cisco as the protocol for policy enablement across physical and virtual switches.
Southbound interfaces are implemented with some called Service Abstraction Layer (SAL), which talks to the network elements via SNMP and CLI.
Note: Cisco OpFlex is a southbound protocol in a software-defined network (SDN).
NEW QUESTION: 3
You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
Status: Running
Type: Self-Hosted
Version: 4.4.7292.1
Running / Registered Node(s): 1/1
High Availability Enabled: False
Linked Count: 0
Queue Length: 0
Average Queue Duration: 0.00s
The integration runtime has the following node details:
Name: X-M
Status: Running
Version: 4.4.7292.1
Available Memory: 7697MB
CPU Utilization: 6%
Network (In/Out): 1.21KBps/0.83KBps
Concurrent Jobs (Running/Limit): 2/14
Role: Dispatcher/Worker
Credential Status: In Sync
Use the drop-down menus to select the answer choice that completes each statement based on the information presented.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: fail until the node comes back online
We see: High Availability Enabled: False
Note: Higher availability of the self-hosted integration runtime so that it's no longer the single point of failure in your big data solution or cloud data integration with Data Factory.
Box 2: lowered
We see:
Concurrent Jobs (Running/Limit): 2/14
CPU Utilization: 6%
Note: When the processor and available RAM aren't well utilized, but the execution of concurrent jobs reaches a node's limits, scale up by increasing the number of concurrent jobs that a node can run Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime