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NEW QUESTION: 1
Which of the following statements about ATP (Available To Promise) checks for material components are correct? (Choose two)
A. ATP checks can be adjusted to also consider future requirements and receipts.
B. ATP checks can be carried out automatically when releasing a production order.
C. ATP checks are used to evaluate the free capacity on a certain work center.
D. ATP checks are automatically performed during Material Requirements Planning (MRP) runs.
E. ATP checks are mandatory for lead-time scheduling.
Answer: A,B

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
この質問の質問に回答した後、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
世界中にある2,000の店舗からPOSデバイスデータを収集するAzureソリューションを開発しています。 1つのデバイスで、24時間ごとに2メガバイト（MB）のデータを生成できます。各店舗には、データを送信するデバイスが1〜5台あります。
デバイスはAzure Blobストレージに保存する必要があります。デバイスデータは、デバイス識別子に基づいて相互に関連付ける必要があります。今後、さらに出店する予定です。
デバイスデータを受信するソリューションを実装する必要があります。
ソリューション：Azure Notification Hubをプロビジョニングします。すべてのデバイスをハブに登録します。
ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Instead provision an Azure Event Hub. Configure the machine identifier as the partition key and enable capture.
References:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-programming-guide

NEW QUESTION: 3
What is a benefit of data modeling languages like YANG?
A. They create more secure and efficient SNMP OIDs.
B. They enable programmers to change or write their own application within the device operating system.
C. They provide a standardized data structure, which results in configuration scalability and consistency.
D. They make the CLI simpler and more efficient.
Answer: C

NEW QUESTION: 4
Microsoft Azureにラムダアーキテクチャを実装するデータエンジニアです。オープンソースのビッグデータソリューションを使用して、データを収集、処理、および保守します。分析データストアのパフォーマンスが低下しています。
次の要件を満たすソリューションを実装する必要があります。
データウェアハウジングを提供する
継続的な管理活動を削減
SQLクエリの応答を1秒未満で配信します
要件を満たすには、HDInsightクラスターを作成する必要があります。
どのタイプのクラスターを作成する必要がありますか？
A. Apache HBase
B. Apache Hadoop
C. Apache Spark
D. インタラクティブクエリ
Answer: C
Explanation:
Explanation
Lambda Architecture with Azure:
Azure offers you a combination of following technologies to accelerate real-time big data analytics:
Azure Cosmos DB, a globally distributed and multi-model database service.
Apache Spark for Azure HDInsight, a processing framework that runs large-scale data analytics applications.
Azure Cosmos DB change feed, which streams new data to the batch layer for HDInsight to process.
The Spark to Azure Cosmos DB Connector

Note: Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch processing and stream processing methods, and minimizing the latency involved in querying big data.
References:
https://sqlwithmanoj.com/2018/02/16/what-is-lambda-architecture-and-what-azure-offers-with-its-new-cosmos