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Exam Code: GitHub-Advanced-Security Dumps

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NEW QUESTION: 1
Which two statements are true about OTV? (Choose two.)
A. It can work over any transport that can forward IP packets.
B. It relies on flooding to propagate MAC address reachability information.
C. It uses a full mesh of point-to-multipoint tunnels to prevent head-end replication of multicast traffic.
D. It supports automatic detection of multihoming.
Answer: A,D
Explanation:
The overlay nature of OTV allows it to work over any transport as long as this transport can forward IP packets. Any optimizations performed for IP in the transport will benefit the OTV encapsulated traffic.
As part of the OTV control protocol, automatic detection of multihoming is included. This feature enables the multihoming of sites without requiring additional configuration or protocols
Reference: http://www.cisco.com/c/en/us/products/collateral/switches/nexus-7000-seriesswitches/white_paper_c11-574984.html
Topic 5, Infrastructure Security

NEW QUESTION: 2
You need to implement a model development strategy to determine a user's tendency to respond to an ad.
Which technique should you use?
A. Use a Split Rows module to partition the data based on distance travelled to the event.
B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
C. Use a Relative Expression Split module to partition the data based on centroid distance.
D. Use a Split Rows module to partition the data based on centroid distance.
Answer: C
Explanation:
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

NEW QUESTION: 3
Smart Predictはどのグローバルパフォーマンス指標で予測モデルを分割しますか? (この質問には2つの正解があります)
A. 予測統計
B. 予測精度
C. 予測の信頼度
D. 予測力
Answer: B,C


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