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NEW QUESTION: 1
Which two touch gestures require a multi-touch device? (Choose two.)
A. pinch
B. hold
C. tap
D. spread
Answer: A,C

NEW QUESTION: 2
Microsoft AzureでLambdaアーキテクチャを使用してソリューションを開発しています。
テストレイヤーのデータは、次の要件を満たしている必要があります。
データストレージ：
*リポジトリ（またはさまざまな形式の大量の大容量ファイル）として機能します。
*ビッグデータ分析ワークロード用に最適化されたストレージを実装します。
*階層構造を使用してデータを整理できるようにします。
バッチ処理：
*メモリ内の計算処理にはマネージドソリューションを使用します。
* Scala、Python、およびRプログラミング言語をネイティブにサポートします。
*クラスターを自動的にサイズ変更および終了する機能を提供します。
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
Lambdaアーキテクチャを構築するには、正しいテクノロジーを特定する必要があります。
どのテクノロジーを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

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.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

NEW QUESTION: 3
Click the Exhibit button.
-- Exhibit -
May 20 22:27:04.010297 OSPF rcvd Hello 192.168.0.2 -> 224.0.0.5 (ge-1/1/2.0 IFL 343
area 0.0.0.0)
May 20 22:27:04.010320 Version 2, length 44, ID 10.10.10.1, area 0.0.0.1
May 20 22:27:04.010342 checksum 0x166d, authtype 0
May 20 22:27:04.010365 mask 255.255.255.252, hello_ivl 10, opts 0x12, prio 128
May 20 22:27:04.010387 dead_ivl 40, DR 192.168.0.2, BDR 0.0.0.0
May 20 22:27:07.891698 OSPF periodic xmit from 192.168.0.1 to 224.0.0.5 (IFL
2684276180 area 0.0.0.0)
May 20 22:27:12.749988 OSPF packet ignored:area mismatch (0.0.0.1) from 192.168.0.2 on intf ge-1/1/2.0 area 0.0.0.0 May 20 22:27:12.750071 OSPF rcvd Hello 192.168.0.2 -> 224.0.0.5 (ge-1/1/2.0 IFL 343
area 0.0.0.0) May 20 22:27:12.750095 Version 2, length 44, ID 10.10.10.1, area 0.0.0.1 May 20 22:27:12.750116 checksum 0x166d, authtype 0 May 20 22:27:12.750139 mask 255.255.255.252, hello_ivl 10, opts 0x12, prio 128 May 20 22:27:12.750161 dead_ivl 40, DR 192.168.0.2, BDR 0.0.0.0 May 20 22:27:14.393182 OSPF rcvd Hello 192.168.0.2 -> 224.0.0.5 (ge-1/1/2.0 IFL 343
area 0.0.0.0) May 20 22:27:14.393262 Version 2, length 44, ID 10.10.10.1, area 0.0.0.0 May 20 22:27:14.393284 checksum 0x0, authtype 0 May 20 22:27:14.393307 mask 255.255.255.252, hello_ivl 50, opts 0x12, prio 128 May 20 22:27:14.393368 dead_ivl 200, DR 0.0.0.0, BDR 0.0.0.0 May 20 22:27:14.393399 OSPF packet ignored:our router ID received from 192.168.0.2 on
intf ge-1/1/2.0 area 0.0.0.0 -- Exhibit -
Referring to the exhibit, which two OSPF adjacency problems are present? (Choose two.)
A. area type mismatch
B. hello interval mismatch
C. duplicate router ID
D. area number mismatch
Answer: C,D

NEW QUESTION: 4
4 개의 500GB EBS 프로비저닝 IOPS 볼륨이 연결된 EC2 인스턴스를 시작했습니다. EC2 인스턴스는 EBS 최적화되었으며 EC2와 EBS간에 500Mbps 처리량을 지원합니다. 네 개의 EBS 볼륨은 단일 RAID 0 장치로 구성되며 프로비저닝 된 각 IOPS 볼륨은 4,000 IOPS (4,000 16KB 읽기 또는 쓰기)로 프로비저닝되어 인스턴스에 총 16,000 개의 임의 IOPS가 제공됩니다. EC2 인스턴스는 처음에 예상 된 16,000 IOPS 임의의 읽기 및 쓰기 성능을 제공합니다. 언젠가 나중에 인스턴스의 총 임의 I / O 성능을 높이기 위해 500GB EBS 프로비저닝 IOPS 볼륨 2 개를 RAID에 추가합니다. 각 볼륨은 원래 4 개와 같은 4,000 개의 IOP에 프로비저닝되어 EC2 인스턴스에 총 24,000 개의 IOPS가 제공됩니다. 모니터링에 따르면 EC2 인스턴스 CPU 사용률이 50 %에서 70 %로 증가했지만 인스턴스 수준에서 측정 된 총 임의 IOPS는 전혀 증가하지 않습니다.
문제와 올바른 해결책은 무엇입니까?
A. RAID 0은 약 4 개의 장치로 선형 확장됩니다. RAID 0을 4 개의 EBS 프로비저닝 된 IOPS 볼륨과 함께 사용하지만 프로비저닝 된 각 IOPS EBS 볼륨을 6,000 IOPS로 늘리십시오.
B. EBS-Optimized 처리량은 활용할 수있는 총 IOPS를 제한합니다. 보다 큰 처리량을 제공하는 EBS 최적화 인스턴스를 사용하십시오.
C. 작은 블록 크기로 인해 성능 저하가 발생하여 I / O 처리량이 제한됩니다. 처리량을 높이기 위해 64KB 블록을 사용하도록 인스턴스 장치 드라이버 및 파일 시스템을 구성하십시오.
D. 표준 EBS 인스턴스 루트 볼륨은 총 IOPS 속도를 제한합니다. 인스턴스 루트 볼륨을 500GB 4,000 프로비저닝 IOPS 볼륨으로 변경하십시오.
E. 더 큰 저장소 볼륨은 높은 프로비저닝 된 IOPS 속도를 지원합니다. 6 개의 EBS 볼륨 각각의 프로비저닝 된 볼륨 저장 공간을 1TB로 늘리십시오.
Answer: D

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