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NEW QUESTION: 1
管理者は新しいサーバーを購入します。新しいサーバーにはXeonプロセッサが1つしかありません。 このサーバーは、
A. 冗長サーバー。
B. シングルコアサーバ。
C. マルチコアサーバ。
D. 負荷分散サーバー。
Answer: C

NEW QUESTION: 2
An Order microservice and a Fulfillment microservice are being designed to communicate with their dients through message-based integration (and NOT through API invocations).
The Order microservice publishes an Order message (a kind of command message) containing the details of an order to be fulfilled. The intention is that Order messages are only consumed by one Mute application, the Fulfillment microservice.
The Fulfilment microservice consumes Order messages, fulfills the order described therein, and then publishes an OrderFulfilted message (a kind of event message). Each OrderFulfilted message can be consumed by any interested Mule application, and the Order microservice is one such Mute application.
What is the most appropriate choice of message broker(s) and message destination(s) in this scenario?
A. Older messages are sent directly to the Fulfillment microservices
OrderFulfilled messages are sent directly to the Order microservice
The Order microservice Interacts with one AMQP-compatible message broker and the Fulfillment microservice Interacts with a different AMQP-compatible message broker, so that both message brokers can be chosen and scaled to best support the toad each microservice
B. Order messages are sent to a JMS queue OrderFulfilled messages are sent to a JMS topic The Order microservice Interacts with one JMS provider (message broker) and the Fulfillment microservice interacts with a different JMS provider, so that both message brokers can be chosen and scaled to best support the load of each microservice
C. Order messages are sent to a JMS queue OrderFulfilled messages are sent to a JMS topic Both microservices Interact with the same JMS provider (message broker) Instance, which must therefore scale to support the load of both microservices
D. Order messages are sent to an Anypoint MQ exchange
OrderFulfilted messages are sent to an Anypoint MQ queue
Both microservices interact with Anypoint MQ as the message broker, which must therefore scale to support the toad of both microservices
Answer: B

NEW QUESTION: 3
これはケーススタディです。ケーススタディは個別にタイミングが取られません。各ケースを完了したいだけの試験時間を使用できます。ただし、この試験に関する追加のケーススタディとセクションがある場合があります。時間を管理して、指定された時間内にこの試験に含まれるすべての質問を完了できるようにする必要があります。
ケーススタディに含まれる質問に答えるには、ケーススタディで提供される情報を参照する必要があります。ケーススタディには、ケーススタディで説明されているシナリオに関する詳細情報を提供する展示物やその他のリソースが含まれている場合があります。このケーススタディでは、各質問は他の質問から独立しています。
このケーススタディの最後に、レビュー画面が表示されます。この画面では、試験の次のセクションに進む前に、回答を確認して変更を加えることができます。新しいセクションを開始した後、このセクションに戻ることはできません。
ケーススタディを開始するには
このケーススタディの最初の質問を表示するには、[次へ]ボタンをクリックします。質問に答える前に、左ペインのボタンを使用して、ケーススタディの内容を調べてください。これらのボタンをクリックすると、ビジネス要件などの情報が表示されます。既存の環境、および問題の説明。ケーススタディに[すべての情報]タブがある場合、表示される情報は後続のタブに表示される情報と同じであることに注意してください。質問に答える準備ができたら、質問ボタンをクリックして質問に戻ります。
バックグラウンド
Salesという名前のデータベースがあります。
Customerテーブルには、顧客が最後に注文した日付を格納する列が含まれています。
Leadsという名前のテーブルを作成する予定です。 Leadsテーブルには、約20,000のレコードが含まれると予想されます。
Leadsテーブルのストレージ要件は最小限に抑える必要があります。
テーブル

3番目の標準形式に準拠するように、テーブルデザインの変更を開始する必要があります。
テーブルごとにどの列を削除する必要がありますか？ 答える？ 適切な列名を正しい場所にドラッグします。 各列名は、1回以上使用することも、まったく使用しないこともできます。 コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。

Answer:
Explanation:

Explanation

Box 1: SupplierName
SupplierName should be moved to a separate Supplier table.
Box 2: ProductName
ProductName is already defined in the Products table.

NEW QUESTION: 4
You plan to ingest streaming social media data by using Azure Stream Analytics. The data will be stored in files in Azure Data Lake Storage, and then consumed by using Azure Datiabricks and PolyBase in Azure Synapse Analytics.
You need to recommend a Stream Analytics data output format to ensure that the queries from Databricks and PolyBase against the files encounter the fewest possible errors. The solution must ensure that the tiles can be queried quickly and that the data type information is retained.
What should you recommend?
A. Parquet
B. CSV
C. JSON
D. Avro
Answer: D
Explanation:
Explanation
The Avro format is great for data and message preservation.Avro schema with its support for evolution is essential for making the data robust for streaming architectures like Kafka, and with the metadata that schema provides, you can reason on the data. Having a schema provides robustness in providing meta-data about the data stored in Avro records which are self- documenting the data.References:
http://cloudurable.com/blog/avro/index.html