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NEW QUESTION: 1
The technique of subdividing project deliverables into smaller, more manageable components until the work and deliverables are defined to the work package level is called:
A. Create WBS.
B. a control chart.
NEW QUESTION: 2
애플리케이션은 데이터 지속성을 위해 두 개의 AZ와 다중 AZ RDS 인스턴스에 배포된 웹 / 애플리케이션 서버의 Auto Scaling 그룹 앞에서 ELB를 사용하고 있습니다. 데이터베이스 CPU는 종종 사용량이 80 % 이상이고 데이터베이스에서 90 %의 I / O 작업을 읽습니다. 성능을 향상시키기 위해 최근에 자주 단일 DB 쿼리 결과를 캐시하기 위해 단일 노드 Memcached ElastiCache 클러스터를 추가했습니다. 다음 주에는 전체 작업량이 30 % 증가 할 것으로 예상됩니다.
예상되는 추가로드로 애플리케이션의 고 가용성을 유지하려면 아키텍처에서 무엇을 변경해야 합니까? 왜?
A. 아니요, 캐시 노드가 실패하면 가용성에 영향을 주지 않고 DB에서 항상 동일한 데이터를 얻을 수 있습니다.
B. 예. 캐시 노드에 장애가 발생하면 RDS 인스턴스가 로드를 처리할수 없으므로 서로 다른 AZ에 2 개의 Memcached ElastiCache 클러스터를 배포해야 합니다.
C. 예. 하나의 캐시 노드가 실패 할 경우로드를 처리하려면 RDS DB 마스터 인스턴스와 동일한 AZ에 두개의 노드가 있는 Memcached ElastiCache 클러스터를 배포해야 합니다.
D. 아니요, 캐시 노드에 장애가 발생하면 자동 ElastiCache 노드 복구 기능이 가용성에 미치는 영향을 방지합니다.
A single-node Memcached ElastiCache cluster failure is nothing but a total failure. (Even though AWS will automatically recover the failed node, there are no other nodes in the cluster)
http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/BestPractices.html Mitigating Node Failures To mitigate the impact of a node failure, spread your cached data over more nodes. Because Memcached does not support replication, a node failure will always result in some data loss from your cluster.
When you create your Memcached cluster you can create it with 1 to 20 nodes, or more by special request. Partitioning your data across a greater number of nodes means you'll lose less data if a node fails. For example, if you partition your data across 10 nodes, any single node stores approximately 10% of your cached data. In this case, a node failure loses approximately
10% of your cache which needs to be replaced when a replacement node is created and provisioned.
Mitigating Availability Zone Failures
To mitigate the impact of an availability zone failure, locate your nodes in as many availability zones as possible. In the unlikely event of an AZ failure, you will lose only the data cached in that AZ, not the data cached in the other AZs.
NEW QUESTION: 3
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
NEW QUESTION: 4
Which VPN technology allows remote sites with dynamic IP addresses to connect to a central hub?
A. static IPsec tunnels
B. site-to-site VPN
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