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NEW QUESTION: 1
一般データ保護規則（GDPR）で定義されている個人データは、いくつかのタイプに分類できます。
「人の人種的または民族的背景、政治的/哲学的/宗教的見解、意見、組合所属、健康または性的習慣に関連するデータを直接的または間接的に明らかにするデータ。」どのような種類の個人データが記述されていますか？
A. 間接的な個人データ
B. 機密の個人データ
C. 直接の個人データ
D. 仮名データ
Answer: B
NEW QUESTION: 2
毎日200,000個の新しいファイルを生成するAzureStorageアカウントがあります。ファイル名の形式は次のとおりです。
{YYYY} / {MM} / {DD} / {HH} / {CustomerID} .csv。
ストレージアカウントからAzureDataLakeに1時間に1回新しいデータを読み込むAzureDataFactoryソリューションを設計する必要があります。このソリューションでは、ロード時間とコストを最小限に抑える必要があります。
ソリューションをどのように構成する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります
Answer:
Explanation:
Explanation
Box 1: Incremental load
When you start to build the end to end data integration flow the first challenge is to extract data from different data stores, where incrementally (or delta) loading data after an initial full load is widely used at this stage.
Now, ADF provides a new capability for you to incrementally copy new or changed files only by LastModifiedDate from a file-based store. By using this new feature, you do not need to partition the data by time-based folder or file name. The new or changed file will be automatically selected by its metadata LastModifiedDate and copied to the destination store.
Box 2: Tumbling window
Tumbling window triggers are a type of trigger that fires at a periodic time interval from a specified start time, while retaining state. Tumbling windows are a series of fixed-sized, non-overlapping, and contiguous time intervals. A tumbling window trigger has a one-to-one relationship with a pipeline and can only reference a singular pipeline.
Reference:
https://azure.microsoft.com/en-us/blog/incrementally-copy-new-files-by-lastmodifieddate-with-azure-data-factor
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger
NEW QUESTION: 3
レポート作成に使用する新しいデータベーステーブルを設計しています。
次の文を使用してテーブルを定義します。
最小限の記憶域を使用してデータをテーブルに格納する必要があります。
どのストレージオプションを使うべきですか？
A. a nonclustered index
B. a clustered columnstore index
C. In-Memory OLTP
D. a clustered index
Answer: B
Explanation:
Columnstore indexes work well for mostly read-only queries that perform analysis on large data sets. This would fit this scenario as the table will be used for reporting.
Columnstore Index benefits include Columnstore Index benefits high compression rates, which improve query performance by using a smaller in-memory footprint. In turn, query performance can improve because SQL Server can perform more query and data operations in-memory.
Use the columnstore index to achieve up to 10x query performance gains over traditional row-oriented storage, and up to 7x data compression over the uncompressed data size.
References: https://msdn.microsoft.com/en-us/library/gg492088(v=sql.120).aspx
NEW QUESTION: 4
You are designing a load plan in which you must run Mappings A and B one after the other while running Mapping C at the same time. Which option represents the steps to accomplish this? (Choose the best answer.)
A. Add a parallel step, add two parallel steps underneath it, then add A and B tooneparallel step, and C to the other.
B. Add a parallel step, add two serial steps underneath it, then add A and B to one serial step, and C to the other.
C. Add a parallel step, add three serial steps underneath it, then addAto the first parallel step, B to the second one, and C to the last one.
D. Create a scenario from the variable and add the scenario to the load plan to create a case step.
Answer: A
Explanation:
(https://docs.oracle.com/middleware/1212/odi/ODIDG/loadplans.htm#ODIDG1356)