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NEW QUESTION: 1
DRAG DROP
Answer:
Explanation:
NEW QUESTION: 2
Your customer needs the data that is generated from social media such as Facebook and
Twitter, and the customer's website to be consumed and sent to an HDFS directory for analysis by the marketing team.
Identify the architecture that you should configure.
A. multiple flume agents with collectors that output to a logger that writes to the Oracle
Loader for Hadoop agent
B. multiple flume agents with sinks that write to a consolidated source with a sink to the customer's HDFS directory
C. multiple HDFS agents that write to a consolidated HDFS directory
D. a single flume agent that collects data from the customer's website, which is connected to both Facebook and Twitter, and writes via the collector to the customer's HDFS directory
E. a single HDFS agent that collects data from the customer's website, which ls connected to both Facebook and Twitter, and writes via the Hive to the customer's HDFS directory
Answer: B
Explanation:
Apache Flume - Fetching Twitter Data. Flume in this case will be responsible for capturing the tweets from Twitter in very high velocity and volume, buffer them in memory channel
(maybe do some aggregation since we're getting JSONs) and eventually sink them into
HDFS.
References: https://www.tutorialspoint.com/apache_flume/fetching_twitter_data.htm
NEW QUESTION: 3
お客様は、ログストリーム（アクセスログ、アプリケーションログ、セキュリティログなど）を単一のシステムに統合することを望んでいます。統合された顧客は、これらのログをヒューリスティックに基づいてリアルタイムで分析したいと考えています。時々、顧客はヒューリスティックを検証する必要があり、過去12時間から抽出されたデータサンプルに戻る必要があります。
顧客の要件を満たす最適なアプローチは何ですか？
A. EC2 syslogdサーバーのAuto Scalingグループをセットアップし、S3にログを保存し、EMRを使用してログにヒューリスティックを適用します
B. すべてのログイベントをAmazon SQSに送信し、EC2サーバーのAuto Scalingグループをセットアップしてログを消費し、ヒューリスティックを適用します。
C. すべてのログイベントをAmazon Kinesisに送信し、ログにヒューリスティックを適用するクライアントプロセスを開発します
D. カスタムログを受信するようにAmazon CloudTrailを設定し、EMRを使用してログにヒューリスティックを適用します
Answer: C
Explanation:
説明
Amazon Kinesisストリームのスループットは、ストリーム内のシャードの数を増やすことにより制限なく拡張できるように設計されています。ただし、Amazon Kinesis Streamsを使用する際に留意する必要がある特定の制限があります。
デフォルトでは、ストリームのレコードは、ストリームに追加されてから最大24時間アクセスできます。
拡張データ保持を有効にすることにより、この制限を最大7日間に引き上げることができます。
1つのレコード内のデータBLOB（Base64エンコード前のデータペイロード）の最大サイズは1メガバイト（MB）です。
各シャードは、1秒あたり最大1000個のPUTレコードをサポートできます。
他のAPIレベルの制限の詳細については、「Amazon Kinesis Streamsの制限」を参照してください。
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