DP-203日本語 無料問題集「Microsoft Data Engineering on Microsoft Azure (DP-203日本語版)」

解説: (JPNTest メンバーにのみ表示されます)

解説: (JPNTest メンバーにのみ表示されます)

Explanation:

Explanation:

Explanation:

Explanation:

Box 1: Denormalize to a second normal form
Denormalization is the process of transforming higher normal forms to lower normal forms via storing the join of higher normal form relations as a base relation. Denormalization increases the performance in data retrieval at cost of bringing update anomalies to a database.
Box 2: New identity columns
The collapsing relations strategy can be used in this step to collapse classification entities into component entities to obtain #at dimension tables with single-part keys that connect directly to the fact table. The single- part key is a surrogate key generated to ensure it remains unique over time.
Example:

Note: A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.
Reference:
https://www.mssqltips.com/sqlservertip/5614/explore-the-role-of-normal-forms-in-dimensional-modeling/
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables- identity

Explanation:

Explanation:

Explanation:

Explanation:

Box 1: Tumbling window
To be able to use the Delay parameter we select Tumbling window.
Box 2:
Recurrence: 30 minutes, not 32 minutes
Delay: 2 minutes.
The amount of time to delay the start of data processing for the window. The pipeline run is started after the expected execution time plus the amount of delay. The delay defines how long the trigger waits past the due time before triggering a new run. The delay doesn't alter the window startTime.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Storage1 provides a hierarchical namespace: Yes
Files from March 2025 will be included: No
Only files that have a Parquet file extension will be included: Yes
Query Breakdown
* Data Source:
* The OPENROWSET function queries data stored in Azure Data Lake Storage Gen2 (storage1) using the serverless SQL pool in Synapse Analytics.
* The data is stored in Parquet files in the folder structure data/orders/year=YYYY/month=MM/.
* Query Filter:
* The filter conditions in the query are:
* customerorders.filepath(1) = '2024': Limits the query to files in the folder year=2024.
* customerorders.filepath(2) IN ('3', '4'): Limits the query to files in the subfolders month=3 or month=4.
* File Format:
* The FORMAT = 'parquet' clause specifies that only Parquet files will be queried.
Statements Analysis
* Storage1 provides a hierarchical namespace.aswer: Yes
* Azure Data Lake Storage Gen2 supports a hierarchical namespace, which enables folder-based organization.
* The folder structure (e.g., data/orders/year=2024/month=3/) demonstrates the use of a hierarchical namespace.
* Files from March 2025 will be included.aswer: No
* The query explicitly filters for year=2024, so files from 2025 will not be included in the results.
* Only files that have a Parquet file extension will be included.aswer: Yes
* The FORMAT = 'parquet' clause in the query ensures that only Parquet files are queried. Files with other extensions (e.g., .csv or .json) will not be included.