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NEW QUESTION: 1
サブスクライバーは、コンバージョントラッキングがオンの状態で電子メールを開き、リンクをクリックしますが、購入はしません。翌日、このサブスクライバーは、フォローアップオファーを含むコンバージョントラッキングがオンになっている2番目の電子メールを受信し、リンクをクリックしますが、それでも購入しません。サブスクライバーはURLをブックマークし、3日目にWebページに戻って、最終的に購入します。
コンバージョンCookieが削除されていないと仮定すると、トランザクションはどのように追跡または帰属されますか？
A. 購入トランザクションは両方の電子メールキャンペーンに起因します。
B. 最終的な購入は、Webページに直接アクセスして行われたため、購入はWebトランザクションと見なされ、電子メールの帰属は登録されません。
C. 購入トランザクションは、受信者が開いてクリックした最初のキャンペーンに起因します。
D. 購入トランザクションは、受信者が開いてクリックした2番目のキャンペーン（最新）に起因します。
Answer: D
NEW QUESTION: 2
Based on the chart, while completing the project, a team member tells you that the team forgot something during planning. Activity F needs the results of activity E before it can begin. Taking this new dependency into account, what would be the effect on the project?
A. It would delay activity F.
B. It would increase the critical path.
C. There would be no effect.
D. Communication would be more complex.
Answer: D
NEW QUESTION: 3
Which three feature sets are used in Stackwise technology? (Choose three.)
A. LAN Base
B. IP Services
C. IP Base
D. LAN Agent
E. WAN Services
F. ARP Base
Answer: A,B,C
Explanation:
The switch port monitor provides data on network switches by port in use, CPU load, memory used, etc. You can even drill down into specific port data to obtain active and historical port utilization information. These reports are integral for meeting compliance needs. As a best practice for safeguarding your network from security risks, accurate information on unused switch ports helps the administrator keep these ports closed.
NEW QUESTION: 4
Which two statements are true about splitting a project data set into training and testing?
A. Use the partition node in SPSS Modeler to split your data to training and testing samples.
B. In order to create a testing sample out of a larger existing data-set a new smaller data-set needs to be created before training in python with SparkMLLib.
C. Use the restructure node in SPSS Modeler to split your data to training and testing samples.
D. A random seed can be used to insure consistent random training data.
E. Larger proportion of training data runs less risk of over-fitting.
Answer: C,E