What is the highest normal form of the relation(s) shown in the exhibit?
A. Third normal form
B. No normal form
C. First normal form
D. Second normal form
Answer: C
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NEW QUESTION: 1
What is the highest normal form of the relation(s) shown in the exhibit?
A. Third normal form
B. No normal form
C. First normal form
D. Second normal form
Answer: C
NEW QUESTION: 2
Hinweis: Diese Frage ist Teil einer Reihe von Fragen, die dasselbe Szenario darstellen. Jede Frage in der Reihe enthält eine eindeutige Lösung, mit der die angegebenen Ziele erreicht werden können. Einige Fragensätze haben möglicherweise mehr als eine richtige Lösung, während andere möglicherweise keine richtige Lösung haben.
Nachdem Sie eine Frage in diesem Abschnitt beantwortet haben, können Sie NICHT mehr darauf zurückgreifen. Infolgedessen werden diese Fragen nicht im Überprüfungsbildschirm angezeigt.
Sie haben ein Azure-Abonnement, das die in der folgenden Tabelle aufgeführten Ressourcen enthält.
VM1 stellt eine Verbindung zu VNET1 her.
Sie müssen VM1 mit VNET2 verbinden.
Lösung: Sie löschen VM1. Sie erstellen VM1 neu und erstellen dann eine neue Netzwerkschnittstelle für VM1.
Erfüllt dies das Ziel?
A. Ja
B. Nein
Answer: A
Explanation:
Instead you should delete VM1. You recreate VM1, and then you add the network interface for VM1.
Note: When you create an Azure virtual machine (VM), you must create a virtual network (VNet) or use an existing VNet. You can change the subnet a VM is connected to after it's created, but you cannot change the VNet.
References:
https://docs.microsoft.com/en-us/azure/virtual-machines/windows/network-overview
NEW QUESTION: 3
If a role is added to Sales Team, then it will get added to which of the following teams?
A. Case Team
B. Account Team
Answer: B
NEW QUESTION: 4
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