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NEW QUESTION: 1
Menu option to expose custom fields in FI is
A. HMIPT
B. ECF
C. HFITMPL
D. CST
Answer: B

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。いくつかの質問セットには複数の正しいソリューションがあり、他のソリューションには正しいソリューションがない場合があります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Microsoft 365サブスクリプションがあります。
一部の外部ユーザーがMicrosoft SharePointサイトのコンテンツにアクセスしたことがわかりました。 SharePointの共有ポリシーを変更して、組織外で共有しないようにします。
SharePoint共有ポリシーが将来変更された場合、通知を受ける必要があります。
解決策：SharePointサイトからアラートを作成します。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B

NEW QUESTION: 3
-- Exhibit -

-- Exhibit -
Sentence We will be hosting the next workshop on thursday, October 15.
Which correction should be made to sentence 5?
A. remove will
B. change hosting to host
C. insert a comma after October
D. no correction is necessary
E. change thursday to Thursday
Answer: E

NEW QUESTION: 4
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
A. Reduce complexity of the MapReduce APIs.
B. Standardize on a single MapReduce API.
C. Resource pressure on the JobTracker.
D. HDFS latency.
E. Single point of failure in the NameNode.
F. Ability to run frameworks other than MapReduce, such as MPI.
Answer: C,F
Explanation:
YARN (Yet Another Resource Negotiator), as an aspect of Hadoop, has two major kinds of benefits:
*(D) The ability to use programming frameworks other than MapReduce. / MPI (Message Passing Interface) was mentioned as a paradigmatic example of a MapReduce alternative
*Scalability, no matter what programming framework you use.
Note:
*The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.
*(B) The central goal of YARN is to clearly separate two things that are unfortunately smushed together in current Hadoop, specifically in (mainly) JobTracker:
/ Monitoring the status of the cluster with respect to which nodes have which resources available. Under YARN, this will be global. / Managing the parallelization execution of any specific job. Under YARN, this will be done separately for each job. The current Hadoop MapReduce system is fairly scalable - Yahoo runs 5000 Hadoop jobs, truly concurrently, on a single cluster, for a total 1.5 - 2 millions jobs/cluster/month. Still, YARN will remove scalability bottlenecks
Reference: Apache Hadoop YARN - Concepts & Applications