SSAS Interview Questions and Answers
Looking for an SSAS Interview Questions for a fresher or experienced? Well, you are on the right page then. Today SSAS has a good market share and one can set his career in this field very easily with productive results. There are too many opportunities from reputed and organized companies for SQL Server Analysis Services.
Today Agile Testing is a must in every company and if you feel that SSAS Questions would get asked to you then you should refer to this blog. Below are the most commonly asked SSAS Interview Questions and Answers which were asked to fresher as well as experienced. Let us know if you find these questions helpful in achieving your goal.
Most Frequently Asked SSAS Interview Questions
Here in this article, we will be listing frequently asked SSAS Interview Questions and Answers with the belief that they will be helpful for you to gain higher marks. Also, to let you know that this article has been written under the guidance of industry professionals and covered all the current competencies.
OLAP stands for Online Analytical Processing. In every Business application, this technology is used. It helps in performing multidimensional analysis of business data and provides solutions for complex calculations and data modeling.
| OLAP | OLTP |
|---|---|
| OLAP consists of a software tool that is used in Data Analysis | OLTP consists of tools that support Transaction Oriented Applications. |
| It is characterized by a large volume of data. | It is characterized by a huge number of short transactions. |
| In this data, the warehouse is created uniquely | OLTP uses a traditional database |
| Benefits would be the consistency of calculations. | Handles all daily transactions of an organization. |
| Responds in seconds to minutes | Response in milliseconds |
SQL Server Analysis Services data source exists as a connection from which we can import or load required data from the database or data warehouse. It contains information like provider, server name, and database name.
Different data sources supported by it are-
- Oracle
- MS Access
- Teradata
- IBM DB2 and other relational databases
The purpose of setting the logical keys is achieved in the scenario where you have to work on the data from a variety of data sources, that time you have to define the logical relationships manually in DSV to supply those relationships that have been defined automatically.
Unified Dimensional Model acts as a bridge between user and data sources. It is built over physical data sources so that the end-user submits queries against the UDM with the help of tools like Microsoft Excel.
Significances are like
- Improve the user model.
- Gives high-performance queries that support interactive analysis.
We can tune performance in SSAS by the following methods-
- Use Partitions
- Query engine cache should be efficiently used.
- Write efficient multidimensional expressions.
- To reduce the number of records defines effective aggregations.
OLAP Database is also known as Fast Analysis of Shared Multidimensional Information i.e. FASMI. It is because it fulfills the FASMI rules that are described below and hidden in its name only-
- Fast Analysis
- Data should be shared by many users
- Data inside the database must be Multidimensional
- Information (support a large volume of data)
XMLA refers to XML for Analysis and is used for accessing data in analytical systems like OLAP. This standard uses Execute and Discover methods, one allows applications to execute and later allows to fetch the information.
In SSAS KPIs are Key Performance Indicators and are used to estimate if the goals are achieved. A value, goal, and trend can be measured by it.
Star Schemas are better as they use fewer foreign keys, more in a denormalized form which makes them better for performance.
However, both schemas depend upon the size of the data. If the data is small then star schema comes into the process on the other hand if data is large with many relationships between them then you should go for Snowflake Schemas.
There are certain enhancements for SSAS which should be applied in some area like-
- Time Intelligence applies to Cube.
- Account Intelligence applies to Dimension or Cube.
- Custom Aggregation to Dimension or Cube.
- Custom Member Formula again to Dimension or Cube.
- Dimension Intelligence applies to Dimension or Cube.
Data Mart is a subset of the data warehouse or data store that is used to support business needs for particular departments like to support certain reports.
- Dependent
- Independent
- Logical data marts are the three types of data marts.
It is an artificially generated key which is useful when the records don’t have any natural key. And not all tables need surrogate keys. The main reason to use them is they can assure that they act as unique and are represented by an auto-incrementing ID.
With the help of ALTER Command, we can add columns to an existing table. This command allows you to drop a column also. The query that can be used to add a column into the existing one is-
ALTER TABLE dbo.StudentRcords ADD hobbies VARCHAR(50) NULL;
NOTE- You have to make the hobbies column NOT NULL or have to give a default value to it, otherwise the above query won’t work.
Follow these steps to create a cube in SQL Server-
- First Data Source should be created
- Then create a Data Source View.
- Specify Dimensions
- Create a Cube (Cube name, type, and database type should be unique )
- After deploying, process the cube.
The is aggregatable property of an attribute helps in assigning members to each dimension. If you will set its value to FALSE then it will not show to all members.
Perspective is from which the complexity of cubes by hidden elements such as groups, dimensions can be reduced. In short, the slicing of the cube is also the same.
We have retail and hospital data and the user is only allowed to see only hospital data, then we can create perspective according to the situation.