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NEW QUESTION: 1
展示を参照してください。 show spanning-tree inconsistentportsコマンドの出力に基づいて、インターフェイスFastEthernet 0/1およびFastEthernet 0/2についてのどのステートメントが本当ですか。
A. それらはspanning-tree bpdufilter disableコマンドで設定されました。
B. spanning-tree guard loopコマンドで設定されています。
C. spanning-tree bpdufilter enableコマンドで設定されています。
D. spanning-tree guard rootコマンドで構成されています。
E. spanning-tree bpduguard disableコマンドで設定されています。
F. spanning-tree bpduguard enableコマンドで構成されています。
Answer: D
NEW QUESTION: 2
경매 웹 사이트를 통해 사용자는 수집 가능한 품목에 입찰 할 수 있습니다. 경매 규칙에 따르면 각 입찰은 한 번만 접수 된 순서대로 처리되어야 합니다. 현재 구현은 Amazon Kinesis Data Streams에 입찰 레코드를 쓰는 Amazon EC2 웹 서버를 기반으로 합니다. 단일 t2.large 인스턴스에는 Kinesis Data Streams에서 들어오는 입찰을 읽고 각 입찰을 처리하는 입찰 프로세서를 실행하는 cron 작업이 있습니다. 경매 사이트의 인기가 높아지고 있지만 사용자는 일부 입찰이 등록되지 않았다고 불평하고 있습니다.
문제 해결은 수요가 많은 시간 동안 입찰 프로세서가 너무 느려 처리 중 때때로 충돌하며 처리중인 레코드를 추적하지 못하는 경우가 있습니다.
입찰 처리의 안정성을 높이려면 어떤 변화가 있습니까?
A. EC2 인스턴스 유형을 t2.large에서 더 큰 일반 컴퓨팅 인스턴스 유형으로 전환하십시오. Kinesis Data Streams의 IncomingRecords 지표를 기반으로 입찰 프로세서를 실행하는 EC2 인스턴스 수를 확장하는 Auto Scaling 그룹에 입찰 프로세서 EC2 인스턴스를 배치합니다.
B. 웹 애플리케이션을 리팩터링하여 각 수신 입찰을 Kinesis Data Streams 대신 Amazon SQS FIFO 대기열에 게시합니다. 지속적으로 SQS 대기열에 입찰 프로세서를 리팩터링하십시오. 최소 및 최대 크기가 1 인 Auto Scaling 그룹에 입찰 처리 EC2 인스턴스를 배치하십시오.
C. 웹 애플리케이션을 리팩터링하여 각 수신 입찰을 Kinesis Data Streams 대신 Amazon SNS 주제에 게시합니다. 사용자가 입찰을 제출하자마자 각 입찰을 처리하는 AWS Lambda 함수를 트리거하도록 SNS 주제를 구성하십시오.
D. Kinesis Data Streams에 입찰을 게시 할 때 Amazon Kinesis Producer Library (KPL)를 사용하도록 웹 애플리케이션을 리팩터링하십시오. 입찰 프로세서를 리팩터링하여 Kinesis Data Streams의 각 레코드를 읽지 않고 처리하고 처리중인 것으로 플래그를 지정합니다. 각 입찰 처리 실행이 시작될 때 Kinesis Data Streams에서 처리되지 않은 레코드를 검색합니다.
Answer: B
Explanation:
FIFO is better in this case compared to Kinesis, as it guarantee the order of the bid. Min Max 1, is okay as the SQS will hold the queue in case of failure of the instance, till it come back again.
NEW QUESTION: 3
You are designing a Spark job that performs batch processing of daily web log traffic.
When you deploy the job in the production environment, it must meet the following requirements:
* Run once a day.
* Display status information on the company intranet as the job runs.
You need to recommend technologies for triggering and monitoring jobs.
Which technologies should you recommend? To answer, drag the appropriate technologies to the correct locations. Each technology may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Livy
You can use Livy to run interactive Spark shells or submit batch jobs to be run on Spark.
Box 2: Beeline
Apache Beeline can be used to run Apache Hive queries on HDInsight. You can use Beeline with Apache Spark.
Note: Beeline is a Hive client that is included on the head nodes of your HDInsight cluster. Beeline uses JDBC to connect to HiveServer2, a service hosted on your HDInsight cluster. You can also use Beeline to access Hive on HDInsight remotely over the internet.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-livy-rest-interface
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-use-hive-beeline
NEW QUESTION: 4
DRAG DROP
Drag and drop the BGP attributes on the left to the correct category on the right. Not all options will be used.
Select and Place:
Answer:
Explanation: