Questions and Answers: 166
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NEW QUESTION: 1
다음 중 교차 훈련의 위험이 있는 것은 어느 것입니까?
A. 승계 계획에 도움이되지 않습니다.
B. 운영의 연속성 달성에 도움이되지 않습니다.
C. 한 명의 직원이 시스템의 모든 부분을 알고있을 수 있습니다.
D. 한 명의 직원에 대한 의존도를 높입니다.
Answer: C
Explanation:
설명:
교차 훈련을 하는 경우, 먼저 모든 사람이 시스템의 모든 부분을 알고 위험에 노출 될 수 있는 위험을 평가하는 것이 현명합니다. 교차 훈련은 한 명의 직원에 대한 의존도를 줄이는 이점이 있으므로 승계 계획의 일부가 될 수 있습니다. 또한 어떠한 이유로 든 부재시 직원의 백업을 제공하므로 운영의 연속성을 용이하게 합니다.
NEW QUESTION: 2
A. Option D
B. Option B
C. Option C
D. Option A
Answer: A,B
Explanation:
Here is a list of the differences between OSPFv2 and OSPFv3:
* They use different address families (OSPFv2 is for IPv4-only, OSPFv3 can be used for IPv6-only or both protocols
* OSPFv3 introduces new LSA types
* OSPFv3 has different packet format
* OSPFv3 uses different flooding scope bits (U/S2/S1)
* OSPFv3 adjacencies are formed over link-local IPv6 communications
* OSPFv3 runs per-link rather than per-subnet
* OSPFv3 supports multiple instances on a single link, Interfaces can have multiple
IPv6 addresses
* OSPFv3 uses multicast addresses FF02::5 (all OSPF routers), FF02::6 (all OSPF
DRs)
* OSPFv3 Neighbor Authentication done with IPsec (AH)
* OSPFv2 Router ID (RID) must be manually configured, still a 32-bit number
Reference: http://www.networkworld.com/article/2225270/cisco-subnet/ospfv3-for-ipv4-and- ipv6.html
NEW QUESTION: 3
HDInsightクラスターでSparkジョブとして実行されるデータ処理ソリューションを設計しています。このソリューションは、小売業者のオンライン注文に関するほぼリアルタイムの情報を提供するために使用されます。
ソリューションには、概要情報を表示する企業イントラネット上のページを含める必要があります。
概要情報ページは、次の要件を満たしている必要があります。
*製品カテゴリ、価格帯、レビュー範囲ごとにグループ化された現在までの売上の概要を表示します。
*総売上、1日前と比較した売上、1年前と比較した売上を含む売上概要情報を表示します。
*できるだけ早く新しい注文の情報を反映します。
ソリューションの設計を推奨する必要があります。
何をお勧めしますか？回答するには、回答領域で適切な構成を選択します。
Answer:
Explanation:
Explanation
Box 1: DataFrame
DataFrames
Best choice in most situations.
Provides query optimization through Catalyst.
Whole-stage code generation.
Direct memory access.
Low garbage collection (GC) overhead.
Not as developer-friendly as DataSets, as there are no compile-time checks or domain object programming.
Box 2: parquet
The best format for performance is parquet with snappy compression, which is the default in Spark 2.x.
Parquet stores data in columnar format, and is highly optimized in Spark.