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NEW QUESTION: 1
In GLBP, which router will respond to client ARP requests?
A. The active virtual gateway will reply with one of four possible virtual MAC addresses.
B. The GLBP member routers will reply with one of four possible burned in hardware addresses.
C. The active virtual gateway will reply with its own hardware MAC address.
D. All GLBP member routers will reply in round-robin fashion.
Answer: A
Explanation:
GLBP Virtual MAC Address Assignment
A GLBP group allows up to four virtual MAC addresses per group. The AVG is responsible for assigning the virtual MAC
addresses to each member of the group. Other group members request a virtual MAC address after they discover the
AVG through hello messages. Gateways are assigned the next MAC address in sequence. A virtual forwarder that is
assigned a virtual MAC address by the AVG is known as a primary virtual forwarder. Other members of the GLBP group
learn the virtual MAC addresses from hello messages. A virtual forwarder that has learned the virtual MAC address is
referred to as a secondary virtual forwarder.
Reference: http://www.cisco.com/en/US/docs/ios/12_2t/12_2t15/feature/guide/ft_glbp.html
NEW QUESTION: 2
資産の分類と管理において、どのプログラム要素を最初に実装する必要がありますか？
A. リスク評価
B. 評価
C. リスク軽減
D. 分類
Answer: B
Explanation:
説明
保護が必要な資産を特定して理解するために、評価が最初に実行されます。リスク評価は、最初のステップである評価によって選択された情報資産に対する脅威を特定し、定量化するために実行されます。
分類とリスク軽減は、評価に続くステップです。
NEW QUESTION: 3
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?
A. A scatter plot showing the correlation between maximum tree depth and the objective metric.
B. A histogram showing whether the most important input feature is Gaussian.
C. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
D. A scatter plot showing (he performance of the objective metric over each training iteration
Answer: C