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

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. change hosting to host
B. insert a comma after October
C. remove will
D. change thursday to Thursday
E. no correction is necessary
Answer: D

NEW QUESTION: 4
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
A. Reduce complexity of the MapReduce APIs.
B. Single point of failure in the NameNode.
C. Standardize on a single MapReduce API.
D. HDFS latency.
E. Ability to run frameworks other than MapReduce, such as MPI.
F. Resource pressure on the JobTracker.
Answer: E,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