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NEW QUESTION: 1
Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation.
A. JobTracker
B. TaskTracker
C. Secondary NameNode
D. NameNode
E. DataNode
Answer: A
Explanation:
JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop performs following actions(from Hadoop Wiki:)
Client applications submit jobs to the Job tracker. The JobTracker talks to the NameNode to determine the location of the data The JobTracker locates TaskTracker nodes with available slots at or near the data The JobTracker submits the work to the chosen TaskTracker nodes. The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker. A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable. When the work is completed, the JobTracker updates its status.
Client applications can poll the JobTracker for information.
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?
NEW QUESTION: 2
ネットワーク管理者は新しいWebサーバーをインストールし、サーバーに1つのクアッドポートNICが設定されていることに気付きます。 ハードウェアリソースを適切にプロビジョニングするために、NICをどのように設定する必要がありますか?
A. 4つのポートすべてに単一のIPアドレスを割り当てる
B. 1つのポートに複数のIPアドレスを割り当てる
C. 1つのIPアドレスを4ポートNICに割り当てます。
D. 各ポートに少なくとも1つのIPアドレスを割り当てる
Answer: D
NEW QUESTION: 3
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、記載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。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
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。
エラスティックプールを使用するAzure SQL Databaseを設計しています。顧客に関するデータをテーブルに保存する予定です。各レコードは、CustomerIDの値を使用します。
CustomerIDの値に基づいてデータをパーティション分割する戦略を推奨する必要があります。
提案されたソリューション:水平分割を使用して、データをシャードに分離します。
ソリューションは目標を達成していますか?
A. いいえ
B. はい
Answer: B
Explanation:
Explanation
Horizontal Partitioning - Sharding: Data is partitioned horizontally to distribute rows across a scaled out data tier. With this approach, the schema is identical on all participating databases. This approach is also called
"sharding". Sharding can be performed and managed using (1) the elastic database tools libraries or (2) selfsharding.
An elastic query is used to query or compile reports across many shards.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview