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NEW QUESTION: 1
A data transmission is using a multiplexer. The multiplexer assigns each channel to one or more time slots based on the channel's requirements. What multiplexing method is being used?
A. Time-division
B. Manchester
C. Statistical
D. Frequency-division
Answer: A
NEW QUESTION: 2
You are implementing the indexing strategy for a fact table in a data warehouse. The fact table is named Quotes. The table has no indexes and consists of seven columns:
*[ID]
*[QuoteDate]
*[Open]
*[Close]
*[High]
*[Low]
*[Volume]
Each of the following queries must be able to use a columnstore index:
*SELECT AVG ([Close]) AS [AverageClose] FROM Quotes WHERE [QuoteDate]
BETWEEN '20100101' AND '20101231'.
*SELECT AVG([High] - [Low]) AS [AverageRange] FROM Quotes WHERE [QuoteDate]
BETWEEN '20100101' AND '20101231'.
*SELECT SUM([Volume]) AS [SumVolume] FROM Quotes WHERE [QuoteDate]
BETWEEN '20100101' AND '20101231'.
You need to ensure that the indexing strategy meets the requirements. The strategy must also minimize the number and size of the indexes.
What should you do?
A. Create one columnstore index that contains [QuoteDate], [Close], [High], [Low], and
[Volume].
B. Create three coiumnstore indexes:
One containing [QuoteDate] and [Close]
One containing [QuoteDate], [High], and [Low]
One containing [QuoteDate] and [Volume]
C. Create two columnstore indexes:
One containing [ID], [QuoteDate], [Volume], and [Close]
One containing [ID], [QuoteDate], [High], and [Low]
D. Create one columnstore index that contains [ID], [Close], [High], [Low], [Volume], and
[QuoteDate].
Answer: A
Explanation:
Reference: http://msdn.microsoft.com/en-us/library/gg492088.aspx
Reference: http://msdn.microsoft.com/en-us/library/gg492153.aspx
NEW QUESTION: 3
オンラインフォトアルバムアプリには、高品質のディスプレイを備えた複数の画面（デスクトップ、携帯電話、タブレットなど）をサポートする主要な設計機能があります。画像の複数のバージョンを異なる解像度とレイアウトで保存する必要があります。
画像処理Javaプログラムは、画像のサイズと形式に応じて、アップロードごとに平均5秒かかります。各画像アップロードでは、ユーザー、アルバム、写真ラベル、アップロードタイムスタンプの画像メタデータがキャプチャされます。
アプリは次の要件をサポートする必要があります。
毎秒数百のユーザー画像アップロード
10 MBの最大画像アップロードサイズ
1 KBの最大画像メタデータサイズ
サポートされているすべての画面で、画像アップロード後1分以内に最適化された解像度で表示される画像これらの要件を満たすために使用する戦略はどれですか？
A. BLOBデータ型でイメージとメタデータRDSを書き込みます。 AWS Data Pipelineを使用して画像処理を実行し、画像出力をAmazon S3に保存し、メタデータをアプリリポジトリDBに保存します。
B. メタデータを含む画像をAmazon S3にアップロードし、Lambda関数を使用して画像処理を実行し、画像出力をAmazon S3に保存し、メタデータをアプリリポジトリDBに保存します。
C. イメージとメタデータをAmazon Kinesisに書き込みます。 Spark StreamingでAmazon Elastic MapReduce（EMR）を使用して画像処理を実行し、画像出力をAmazon S3に保存し、メタデータをアプリリポジトリDBに保存します。
D. イメージとメタデータをAmazon Kinesisに書き込みます。 Kinesis Client Library（KCL）アプリケーションを使用して画像処理を実行し、画像出力をAmazon S3に保存し、メタデータをアプリリポジトリDBに保存します。
Answer: B
Explanation:
説明
https://aws.amazon.com/blogs/big-data/building-and-maintaining-an-amazon-s3-metadata-index-without-servers