1. Geoffrey is a data modeler at Adventure Works who developed a DirectQuery model that connects to the data warehouse. To improve the query performance of higher-grain sales queries, Geoffrey added an import aggregation table. What else should Geoffrey do to improve query performance of the higher-grain queries? Set related dimension tables as aggregation tables.Set related dimension tables to dual storage mode.Set related dimension tables to import storage mode.

Joshua is a data modeler at Adventure Works who is developing a model for a large data warehouse. The model must enforce RLS, and the Power BI reports that connect to the model should deliver the fastest possible performance. What should Joshua do? Apply rules to dimension tables.Apply rules to hierarchies.Apply rules to fact tables.

As SeaForest's network for farmer customers grows, the company sets up a relational database to replace the existing spreadsheet record of the farmer's information and the sales transactions. This change allows Sea Forest to address the emerging issues about their "Quality of Data" as the amount of data grows. In class, you were introduced to 6 dimensions of the Quality of Data: a. Suggest TWO such potential Quality of Data dimensions that are likely to be violated by using a spreadsheet to manage a large amount of transactional data. Justify your suggestions with a brief explanation and a specific example. Note that your suggestions must be related to the nature of spreadsheets for this specific purpose. (9 marks) b. For each of the two violations given in your answer to part a, explain how the use of a relational database instead of a spreadsheet can avoid such violations. Support each explanation with a specific example of relational database design (eg. How the data is organised to minimise the specific violation). Note that your answer must be about how relational databases may address the short-comings of spreadsheets for managing large amounts of data, rather than the properties of relational databases that are not related to such short-comings. (6 marks) Copy and paste the following answer template onto the answer field. Fill in your answer accordingly. a. Dimension 1: Justification: Example: Dimension 2: Justification: < > Example:

Data warehouse

The deployment of large data warehouses with terabytes or even petabytes of data been crucial to the growth of decision support. All the following explain why EXCEPTdata warehouses have enabled the collection of decision makers in one place.data warehouses have enabled the affordable collection of data for analytics.  data warehouses have assisted the collection of data from multiple sources.data warehouses have assisted the collection of data for data mining

Select Any One Of the Following Options: The deployment of large data warehouses with terabytes or even petabytes of data been crucial to the growth of decision support. All the following explain why EXCEPTdata warehouses have enabled the affordable collection of data for analytics.data warehouses have enabled the collection of decision makers in one place.  data warehouses have assisted the collection of data from multiple sources.data warehouses have assisted the collection of data for data mining