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NEW QUESTION: 1
Azure SQL Databaseインスタンスをデプロイして、アプリケーションをサポートする予定です。 DTUベースの購入モデルを使用する予定です。
データベースのバックアップは30日間利用可能でなければならず、ポイントインタイム復元が可能でなければなりません。
バックアップと復元のポリシーを推奨する必要があります。
目標を達成するための2つの可能な方法は何ですか？それぞれの正解は完全な解決策を提示します。
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。
A. 標準ティアとデフォルトのバックアップ保存ポリシーを使用します。
B. プレミアム層とデフォルトのバックアップ保持ポリシーを使用します。
C. プレミアム層を使用して、長期バックアップ保存ポリシーを構成します。
D. 標準ティアを使用して、長期バックアップ保存ポリシーを構成します。
E. 基本層とデフォルトのバックアップ保存ポリシーを使用します。
Answer: C,D
Explanation:
説明
DTUベースの購入モデルを使用して作成されたデータベースのデフォルトの保持期間は、サービス層によって異なります。
*基本サービスレベルは1週間です。
*標準サービスレベルは5週間です。
*プレミアムサービスレベルは5週間です。
参照：
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-long-term-retention

NEW QUESTION: 2
Which one of the following is NOT a check for Input or Information Accuracy in Software Development security?
A. Range Check
B. Review check
C. Reasonableness check
D. Relationship Check
Answer: B
Explanation:
Explanation/Reference:
Explanation:
There is no such thing as a review check for input validation.
Incorrect Answers:
B: Simple range examines user input for consistency with a minimum/maximum range.
C: A relationship Check test if logically related data elements are compatible. For example that an employee rated as "hourly" gets paid at a rate within the range of $8 and $20.
D: Reasonable indicators are used to judge whether data is within a reasonable range based on metadata.
References:
https://en.wikipedia.org/wiki/Data_validation

NEW QUESTION: 3
loTデバイスからのデータを使用するAl実装のソリューションを設計する予定です。
次の要件を満たすloTデバイス用のデータストレージソリューションを推奨する必要があります。
*データがソリューションにストリーミングされるときに、リアルタイムでデータを照会できます。
*データをソリューションにロードする際の遅延を最小限に抑えます。
推奨事項に何を含めるべきですか？
A. インメモリOLTPが有効になっているMicrosoft Azure SQLデータベース
B. Microsoft Azureテーブルストレージソリューション
C. Microsoft Azure HDInsight Hadoopクラスター
D. Microsoft Azure HDInsight Rサーバークラスター
Answer: C
Explanation:
You can use HDInsight to process streaming data that's received in real time from a variety of devices.
Internet of Things (IoT)
You can use HDInsight to build applications that extract critical insights from data. You can also use Azure Machine Learning on top of that to predict future trends for your business.
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-introduction