HOTSPOT (Drag and Drop is not supported)You have a SQL pool in Azure Synapse.You plan to load data from Azure Blob storage to a staging table. Approximately 1 million rows of data will be loaded daily. The table will be truncated before each daily load.You need to create the staging table. The solution must minimize how long it takes to load the data to the staging table. How should you configure the table? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: HashHash-distributed tables improve query performance on large fact tables. They can have very large numbers of rows and still achieve high performance.Incorrect Answers:Round-robin tables are useful for improving loading speed.Box 2: Clustered columnstoreWhen creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed.Box 3: DateTable partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column. Partition switching can be used to quickly remove or replace a section of a table.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partitionhttps://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute
You are designing a fact table named FactPurchase in an Azure Synapse Analytics dedicated SQL pool. The table contains purchases from suppliers for a retail store. FactPurchase will contain the following columns.FactPurchase will have 1 million rows of data added daily and will contain three years of data. Transact-SQL queries similar to the following query will be executed daily.SELECT SupplierKey, StockItemKey, IsOrderFinalized, COUNT(*) FROM FactPurchase WHERE DateKey >= 20210101 AND DateKey <= 20210131 GROUP By SupplierKey, StockItemKey, IsOrderFinalizedWhich table distribution will minimize query times?
Answer(s): B
Hash-distributed tables improve query performance on large fact tables. To balance the parallel processing, select a distribution column that:-Has many unique values. The column can have duplicate values. All rows with the same value are assigned to the same distribution. Since there are 60 distributions, some distributions can have > 1 unique values while others may end with zero values.-Does not have NULLs, or has only a few NULLs. -Is not a date column.Incorrect Answers:C: Round-robin tables are useful for improving loading speed.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute
HOTSPOT (Drag and Drop is not supported)From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays. The data contains the following columns.You need to design a star schema to support analytical queries of the data. The star schema will contain four tables including a date dimension. To which table should you add each column? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: DimEvent Box 2: DimChannelBox 3: FactEventsFact tables store observations or events, and can be sales orders, stock balances, exchange rates, temperatures, etc
https://docs.microsoft.com/en-us/power-bi/guidance/star-schema
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have an Azure Storage account that contains 100 GB of files. The files contain rows of text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.You plan to copy the data from the storage account to an enterprise data warehouse in Azure Synapse Analytics. You need to prepare the files to ensure that the data copies quickly.Solution: You convert the files to compressed delimited text files. Does this meet the goal?
All file formats have different performance characteristics. For the fastest load, use compressed delimited text files.
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have an Azure Storage account that contains 100 GB of files. The files contain rows of text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.You plan to copy the data from the storage account to an enterprise data warehouse in Azure Synapse Analytics. You need to prepare the files to ensure that the data copies quickly.Solution: You copy the files to a table that has a columnstore index. Does this meet the goal?
Instead convert the files to compressed delimited text files.
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