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NEW QUESTION: 1
Which IBM storage offering scales linearly without hot-spots and delivers insights from
analytics faster with tuning-free data distribution?
A. DS8870
B. LTFS
C. IBM FlashSystem
D. XIV
Answer: B
Explanation:
Reference:
https://www950.ibm.com/events/wwe/impact/impact2014cms.nsf/download/k2afbd72827662766145c2 733fd3/$FILE/Impact2014_3421.pdf
NEW QUESTION: 2
この質問では、下線付きのテキストを評価して、それが正しいかどうかを判断する必要があります。
サービスとしてのソフトウェア（SaaS）ソリューションを実装する場合は、高可用性を構成する必要があります。
手順：下線付きのテキストを確認します。内容が正しい場合は、「変更不要」を選択してください。ステートメントが正しくない場合は、ステートメントを正しくする回答の選択肢を選択してください。
A. スケーラビリティルールの定義
B. SaaSソリューションの構成
C. 変更は必要ありません。
D. SaaSソリューションのインストール
Answer: B
NEW QUESTION: 3
Azure Stream Analytics機能を実装しています。
各要件に対してどのウィンドウ関数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
Box 1: Tumbling
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
Box 2: Hoppping
Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap, so events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.
Box 3: Sliding
Sliding window functions, unlike Tumbling or Hopping windows, produce an output only when an event occurs. Every window will have at least one event and the window continuously moves forward by an € (epsilon). Like hopping windows, events can belong to more than one sliding window.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions