Explanation:

Box1: Process Full:
When Process Full is executed against an object that has already been processed,
Analysis Services drops all data in the object, and then processes the object. This kind of processing is required when a structural change has been made to an object, for example, when an attribute hierarchy is added, deleted, or renamed.
Box 2: Process Default
Detects the process state of database objects, and performs processing necessary to deliver unprocessed or partially processed objects to a fully processed state. If you change a data binding, Process Default will do a Process Full on the affected object.
Box 3:
Not Process Update: Forces a re-read of data and an update of dimension attributes.
Flexible aggregations and indexes on related partitions will be dropped.

Explanation:

Scenario:
Box 1, Processing option:Default
Process Default detects the process state of database objects, and performs processing necessary to deliver unprocessed or partially processed objects to a fully processed state.
If you change a data binding, Process Default will do a Process Full on the affected object.
Note: Processing Method This setting controls whether the deployed objects are processed after deployment and the type of processing that will be performed. There are three processing options:
Default processing (default)
Full processing
None
Box 2, Transactional deployment: False
If this option is False, Analysis Services deploys the metadata changes in a single transaction, and deploys each processing command in its own transaction.
Scenario: The CustomerAnalysis data model will contain a large amount of data and needs to be shared with other developers even if a deployment fails. Each time you deploy a change during development, processing takes a long time.
References:https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional- models/deployment-script-files-specifying-processing-options

Explanation:

Box 1: ProcessData
Processes data only without building aggregations or indexes. If there is data is in the partitions, it will be dropped before re-populating the partition with source data.
Box 2: Process Update
Forces a re-read of data and an update of dimension attributes. Flexible aggregations and indexes on related partitions will be dropped.
References:https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional- models/processing-options-and-settings-analysis-services
Topic 2, Case Study #2
Background
Wide World Importers has multidimensional cubes named SalesAnalysis and
ProductSales. The SalesAnalysis cube is refreshed from a relational data warehouse. You have a Microsoft SQL Server Analysis Services instance that is configured to use tabular mode. You have a tabular data model named CustomerAnalysis.
Sales Analysis
The SalesAnalysis cube contains a fact table named CoffeeSale loaded from a table named FactSale in the data warehouse. The time granularity within the cube is 15 minutes.
The cube is processed every night at 23:00. You determine that the fact table cannot be fully processed in the expected time. Users have reported slow query response times.
The SalesAnalysis model contains tables from a SQL Server database named SalesDB.
You set the DirectQueryMode option to DirectQuery. Data analyst access data from a cache that is up to 24 hours old. Data analyst report performance issues when they access the SalesAnalysis model.
When analyzing sales by customer, the total of all sales is shown for every customer, instead of the customer's sales value. When analyzing sales by product, the correct totals for each product are shown.
Customer Analysis
You are redesigning the CustomerAnalysis tabular data model that will be used to analyze customer sales. You plan to add a table named CustomerPermission to the model. This table maps the Active Directory login of an employee with the CustomerId keys for all customers that the employee manages.
The CustomerAnalysis data model will contain a large amount of data and needs to be shared with other developers even if a deployment fails. Each time you deploy a change during development, processing takes a long time.
Data analysts must be able to analyze sales for financial years, financial quarters, months, and days. Many reports are based on analyzing sales by month.
Product Sales
The ProductSales cube allows data analysts to view sales information by product, city, and time. Data analysts must be able to view ProductSales data by Year to Date (YTD) as a measure. The measure must be formatted as currency, associated with the Sales measure group, and contained in a folder named Calculations.
Requirements
You identify the following requirements:
*Data available during normal business hours must always be up-to-date.
*Processing overhead must be minimized.
*Query response times must improve.
*All queries that access the SalesAnalysis model must use cached data by default.
*Data analysts must be able to access data in near real time.

Analysis Services can be installed in one of three server modes: Multidimensional and Data
Mining (default), Power Pivot for SharePoint, and Tabular.
References:https://docs.microsoft.com/en-us/sql/analysis-services/comparing-tabular-and- multidimensional-solutions-ssas

A reference dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined indirectly to the fact table through a key in another dimension table, as shown in the following illustration.

A reference dimension relationship represents the relationship between dimension tables and a fact table in a snowflake schema design. When dimension tables are connected in a snowflake schema, you can define a single dimension using columns from multiple tables, or you can define separate dimensions based on the separate dimension tables and then define a link between them using the reference dimension relationship setting. The following figure shows one fact table named InternetSales, and two dimension tables called
Customer and Geography, in a snowflake schema.

You can create two dimensions related to the InternetSales measure group: a dimension based on the Customer table, and a dimension based on the Geography table. You can then relate the Geography dimension to the InternetSales measure group using a reference dimension relationship using the Customer dimension.