You are designing a slowly changing dimension (SCD) for supplier data in an Azure Synapse Analytics dedicated SQL pool.You plan to keep a record of changes to the available fields. The supplier data contains the following columns.Which three additional columns should you add to the data to create a Type 2 SCD? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
Answer(s): A,B,E
HOTSPOT (Drag and Drop is not supported)You have a Microsoft SQL Server database that uses a third normal form schema.You plan to migrate the data in the database to a star schema in an Azure Synapse Analytics dedicated SQL pool.You need to design the dimension tables. The solution must optimize read operations.What should you include in the solution? 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: Denormalize to a second normal formDenormalization 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 columnsThe collapsing relations strategy can be used in this step to collapse classification entities into component entities to obtain flat dimension tables with single-part keys that connect directly to the fact table. The singlepart 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.
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-tablesidentity
HOTSPOT (Drag and Drop is not supported)You plan to develop a dataset named Purchases by using Azure Databricks. Purchases will contain the following columns:-ProductID -ItemPrice -LineTotal -Quantity -StoreID -Minute -Month -Hour -Year-DayYou need to store the data to support hourly incremental load pipelines that will vary for each Store ID. The solution must minimize storage costs.How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.Hot Area:
Box 1: partitionByWe should overwrite at the partition level.Example: df.write.partitionBy("y","m","d").mode(SaveMode.Append).parquet("/data/hive/warehouse/db_name.db/" + tableName)Box 2: ("StoreID", "Year", "Month", "Day", "Hour", "StoreID")Box 3: parquet("/Purchases")
https://intellipaat.com/community/11744/how-to-partition-and-write-dataframe-in-spark-without-deletingpartitions-with-no-new-data
You are designing a partition strategy for a fact table in an Azure Synapse Analytics dedicated SQL pool. The table has the following specifications:-Contain sales data for 20,000 products.-Use hash distribution on a column named ProductID.-Contain 2.4 billion records for the years 2019 and 2020.Which number of partition ranges provides optimal compression and performance for the clustered columnstore index?
Each partition should have around 1 millions records. Dedication SQL pools already have 60 partitions. We have the formula: Records/(Partitions*60)= 1 millionPartitions= Records/(1 million * 60)Partitions= 2.4 x 1,000,000,000/(1,000,000 * 60) = 40Note: Having too many partitions can reduce the effectiveness of clustered columnstore indexes if each partition has fewer than 1 million rows. Dedicated SQL pools automatically partition your data into 60 databases. So, if you create a table with 100 partitions, the result will be 6000 partitions.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool
HOTSPOT (Drag and Drop is not supported)You are creating dimensions for a data warehouse in an Azure Synapse Analytics dedicated SQL pool. You create a table by using the Transact-SQL statement shown in the following exhibit.Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: Type 2A Type 2 SCD supports versioning of dimension members. Often the source system doesn't store versions, so the data warehouse load process detects and manages changes in a dimension table. In this case, the dimension table must use a surrogate key to provide a unique reference to a version of the dimension member. It also includes columns that define the date range validity of the version (for example, StartDate and EndDate) and possibly a flag column (for example, IsCurrent) to easily filter by current dimension members.Incorrect Answers:A Type 1 SCD always reflects the latest values, and when changes in source data are detected, the dimension table data is overwritten.Box 2: a business keyA business key or natural key is an index which identifies uniqueness of a row based on columns that exist naturally in a table according to businessrules. For example business keys are customer code in a customer table, composite of sales order header number and sales order item line number within a sales order details table.
https://docs.microsoft.com/en-us/learn/modules/populate-slowly-changing-dimensions-azure-synapse-analytics-pipelines/3-choose-between-dimension-types
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