October 20, 2018

Sreekanth B

Synechron Most Frequently Asked Latest SSAS Interview Questions Answers

What is named query?

Named query in DSV is similar to View in Database. This is used to create Virtual table in DSV which will not impact the underlying database. Named query is mainly used to merge the two or more table in the datasource view or to filter columns of a table.

How you provide security to cube?

By defining roles we provide security to cubes. Using roles we can restrict users from accessing restricted data. Procedure as follows –
Define Role
Set Permission
Add appropriate Users to the role

How much time it take to Process the Cube?

This is Very very important question. This again depends on the SIZE of database,Complexity of the database and your server settings. For database with 50 cr transaction records, it generally takes 3.5 hrs.

How many Calculation you done in Your Project?

I answer more than 5000 and if you tell the same then you are caught unless you are super good in MDX. Best answer for you is “Worked on 50 calculations”.

How will you hide an attribute?

We can hide the attribute by selecting “AttributeHierarchyVisible = False” in properties of the attribute.

 How will you make an attribute not process?

By selecting  “ AttributeHierarchyEnabled = False”, we can make an  attribute not in process.

Why we need named queries?

A named query is used to join multiple tables, to remove unnecessary columns from a table of a database. You can achieve the same in database using Views but this Named Queries will be the best bet whe you don’t have access to create Views in database.

 How will you add a new column to an existing table in data source view?

By using named calculations we can add a new column to an existing table in the data source view. Named Calculation is explained above.

 What is dimension table?

A dimension table contains hierarchical data by which you’d like to summarize. A dimension table contains specific business information, a dimension table that contains the specific name of each member of the dimension. The name of the dimension member is called an “attribute”

The key attribute in the dimension must contain a unique value for each member of the dimension. This key attribute is called “primary key column”

The primary key column of each dimension table corresponding to the one of the key column  in any related fact table.
Synechron Most Frequently Asked Latest SSAS Interview Questions Answers
Synechron Most Frequently Asked Latest SSAS Interview Questions Answers

What is fact table?

A fact table contains the basic information that you wish to summarize. The table that stores the detailed value for measure is called fact table. In simple and best we can define as “The table which contains METRICS” that are used to analyse the business.

It consists of 2 sections

1) Foregine key to the dimesion

2) measures/facts(a numerical value that used to monitor business activity)

What is Factless fact table?

This is very important interview question. The “Factless Fact Table” is a table which is similar to Fact Table except for having any measure; I mean that this table just has the links to the dimensions. These tables enable you to track events; indeed they are for recording events.

Factless fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregatable numeric values or information. They are mere key values with reference to the dimensions from which the stats can be collected

What is star, snowflake and star flake schema?

Star schema: In star schema fact table will be directly linked with all dimension tables. The star schema’s dimensions are denormalized with each dimension being represented by a single table. In a star schema a central fact table connects a number of individual dimension tables.

Snowflake: The snowflake schema is an extension of the star schema, where each point of the star explodes into more points. In a star schema, each dimension is represented by a single dimensional table, whereas in a snowflake schema, that dimensional table is normalized into multiple lookup tables, each representing a level in the dimensional hierarchy. In snow flake schema fact table will be linked directly as well as there will be some intermediate dimension tables between fact and dimension tables.

Star flake: A hybrid structure that contains a mixture of star(denormalized) and snowflake(normalized) schema’s.

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