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NEW QUESTION: 1
シナリオ：Citrix Architectは、新しいXenAppおよびXenDesktop環境を設計する必要があります。次の設計要件が確認されています。
評価フェーズの後半で、アーキテクトは別紙に示されているアクセスマトリックスを作成します。
[Exhibit]ボタンをクリックして、アクセスマトリックスを表示します。

品質保証レビュー中に、アーキテクトは、ユーザーグループの1つが設計要件に適合していないと判断します。
*外部パートナーユーザーは、最新のウイルス対策ソフトウェアを実行している企業のラップトップから接続しない限り、公開リソースへのアクセスを拒否する必要があります。
*外部ユーザーおよびパートナーは、Two-Factor認証を使用して認証します。
* 10分間何も操作しないと、外部の従業員はCitrix Receiver for Webからログオフされます。
*クリップボードマッピングは、外部エンジニアリングユーザーに対して有効になっています。
*会社のデバイスに対して認証されると、内部ユーザーは、公開されたアプリやデスクトップにアクセスするために再度認証することはありません。
識別された設計要件に一致するように、アーキテクトはどのユーザーグループを変更すべきですか
A. パートナー
B. エンジニアリング
C. 販売
D. コールセンター
E. HR
Answer: D

NEW QUESTION: 2
どのコマンドは、バインディングテーブルのステータスを確認するために使用することができますか？
A. IP DHCPプールを表示する
B. IP DHCPスヌーピング統計情報を表示する
C. ip dhcpスヌーピングバインディングを表示する
D. IP DHCPスヌーピングデータベースを表示する
E. IP DHCPスヌーピングを表示する
F. 結合IP DHCPのソースを表示する
Answer: D
Explanation:
Explanation
A device's burned-in address is its MAC address. So by changing it to something else may trick hosts on the network into sending packets to it.

NEW QUESTION: 3
You are responsible for the companynetwork and provide technical support for your company. You are in charge of offering service for a customer named Alex. There are two key performance indicators in Alex's company. That is,purchasing assets without a review of the current license availability and purchasing assets on a per-project basis. Now you get an order from the CIO, according to his requirements, the minimum SAM maturity level represented by both of these indicators should be identified. So of the following maturity levels,which one should be identified?
A. Dynamic maturity level should be identified.
B. Basic maturity level should be identified.
C. Rationalized maturity level should be identified.
D. Standardized maturity level should be identified.
Answer: B

NEW QUESTION: 4
What determines the number of Reduces that run a given MapReduce job on a cluster running MapReduce v1 (MRv1)?
A. It is set by the developer.
B. It is set and fixed by the cluster administrator in mapred-site.xml. The number set always run for any submitted job.
C. It is set by the JobTracker based on the amount of intermediate data.
D. It is set by the Hadoop framework and is based on the number of InputSplits of the job.
Answer: A
Explanation:
Number of Reduces
The right number of reduces seems to be 0.95 or 1.75 * (nodes * mapred.tasktracker.tasks.maximum). At 0.95 all of the reduces can launch immediately and start transfering map outputs as the maps finish. At 1.75 the faster nodes will finish their first round of reduces and launch a second round of reduces doing a much better job of load balancing.
Currently the number of reduces is limited to roughly 1000 by the buffer size for the output files (io.buffer.size * 2 * numReduces << heapSize). This will be fixed at some point, but until it is it provides a pretty firm upper bound.
The number of reduces also controls the number of output files in the output directory, but usually that is not important because the next map/reduce step will split them into even smaller splits for the maps.
The number of reduce tasks can also be increased in the same way as the map tasks, via JobConf's conf.setNumReduceTasks(int num).
Reference:org.apache.hadoop.mapred
Class JobConf