SSAS Developer Interview Questions You Will be Asked [Updated]

SSAS Developer Interview Questions

SSAS Developer Interview Questions – The SQL Server Analysis Services (SSAS) is a Microsoft product used in data mining for business intelligence and transaction processing. Here you can find questions that you will be asked when you seek employment as an SSAS developer. 

What is SSAS?

Microsoft SQL Server Analysis Services or SSAS is a powerful data mining and data warehousing tool that helps businesses and organizations make sense of information dispersed over multiple databases, records, and files. SSAS allows for multidimensional data analysis while supporting tabular analyses as well. SSAS is primarily used in business intelligence, and SSAS developers extend and maximize its functionality in a competitive, fast-evolving, and data-hungry business world. 

Get a Job as an SSAS developer

Every SSAS interview is different because each firm has unique business intelligence paradigms, but certain questions will be asked in almost every SSAS interview. 

Top 12 SSAS Developer Interview Questions

  1. What are the technical specifications of Microsoft’s SSAS?

SSAS is used for analytic processing online. Bundled with the SQL server installer, it uses a multidimensional data structure known as a cube, first implemented in the Microsoft System Center 2012 Service Manager Version. An OLAP cube can analyze data at high speeds because it can efficiently search through large data volumes, retrieve any number of data points and sum up large amounts of data. OLAP cubes are stored in SSAS. Online Transaction Processing (OLTP), another feature of SSAS, allows enterprise users to store large volumes of constantly updated transaction data and records. SSAS is coded using both multidimensional and data mining expression markup. MDX and DMX are used in data-intensive analytics, while SSAS’s built-in scripting language handles databases.

  1. What benefits does SSAS offer?

SSAS provides several methods for creating business intelligence models. Models can be multi-dimensional, pivotal, or tabular, each of which can be configured to fulfill business goals. Users can derive and visualize data from disparate data sources using SSAS. The multidimensional model is the latest, built on open standards, and compatible with many business intelligence software. Multidimensional models are used in OLAP/OLTP. 

  1. What is OLAP and OLTP? How are they different?

OLAP is primarily a tool for data mining and insights that drive business strategy. Data is not continuously updated, and information is not authenticated, though verifiable. OLTP is used to help process and record every transaction and record, thus requiring a continuously updated stream of authenticated data. Business needs and schedules drive the speed of the data stream. 

  1. What is a cube and why is it partitioned?
  • The SSAS hypercube is a multidimensional database comprising measures and dimensions. Unlike tabular data in a relational database, a cube is used to extract data in multiple dimensions. Columnar attributes determine dimensions. Time, location, employee, and customer data can be stored in a dimension. Measures, however, refer only to numeric values and aggregations such as averages, sums, counts, and distinct values.

  • A partition is the physical location of stored cube data. Each cube has at least one partition. A partitioned cube answer queries faster as it contains several preset queries. Queries run even faster on partitions storing aggregate data such as precalculated totals. Partitioning is an efficient and customizable method for managing large cubes. 
  1. When is a measure considered derived, and when is it considered a calculation?

A derived and calculated measure differ only when the calculation is performed. A measure is considered a derivative if it is calculated before data is culled and organized from sources. The cube can contain calculations made on pre-aggregate data. Measures calculated after data aggregation are more accurate but not stored in the cube.

Measures stored in the hypercube are sorted using fact tables and are thus linked with dimensions. Measure groups can also store aggregate data such as distinct counts and can reduce processing times. 

  1. What are the various databases that SSAS can source data from? 

SQL Server Analysis Services support a wide variety of data sources in both multidimensional and relational models. SSAS can capture data from Access and the SQL Server relational database. It can also capture relational databases from Oracle, Teradata, Informix, IBM DB2, and the Sybase Adaptive Server Enterprise. The relational databases can be rendered in the tabular model as well. After installing SQL Server Data Tools and any additional drivers required by individual vendors, data sources can be accessed. 

  1. Is it possible to combine data from multiple data sources? If so, how is it done with SSAS?

SSAS enables users to integrate data from multiple sources into a unified form known as the data source view. The data source view is created from choosing a primary source, after which any number of additional sources can be added to the view. In most cases, the primary data source is the SQL server itself, which allows for adding multiple secondary data sources seamlessly. Tables and data from all sources can be integrated into a single unified and harmonized point of view. 

  1. How does the Data Source View link with multiple data sources?

A data source view is a logical representation of SSAS’s multidimensional model. While the SSAS hypercube consists of dimensions and data structures, the data source view is a schematic representation of the SSAS hypercube. It contains metadata stored in XML format, representing objects selected from multiple data inputs. The metadata is also used to create a relational data store. The data source view can be built on multiple data sources and represent data not present in the source, such as data relationships, keys, object names, columns, and queries. This schematic is mostly created to speed up the process of data modeling in the initial phases. 

  1. What is impersonation and what impersonation options does SSAS allow?

Impersonation enables SSAS to perform various functions by recreating the identity and security context of the client user. Impersonation also allows users with varying levels of control to execute various data mining and analytics operations using SSAS. All but one of these four contexts empower SSAS to perform server-side data services as well. 

  1. Windows Login: SSAS can authenticate the credentials of a Windows user to perform server-side functions on source data.

  2. Service Account: SSAS can also authenticate the service account used to configure the Analysis Service and perform server-side functions.

  3. Current User: Impersonation using the current user’s credentials allows SSAS to perform operations like DMX Open Queries, but it cannot perform server-side operations in this security/identity context. 

  4. Inherit: This option enables the SSAS server to auto-select the impersonation mode best suited for each type of operation. When set to ‘inherit’, SSAS will use the current user credentials to query data mining models and local cubes, while using the service account to perform server-side functions. 
  1. What is a dimension table?

The dimension table is a core component of the SSAS hypercube. A dimension table has information hierarchies in a relational model containing explicit business data, such as records about customers, stores, employees, vendors, and users. Using field attributes, dimensions in SSAS replicate data relations found in the columns of a relational model. Multiple cube dimensions can be harnessed to provide information that guides business choices. Dimensions can be configured to create various schemata or relational models, and their storage models can be both relational and multidimensional. 

  1. What is a factless fact table?

The records in a fact table are immutable, i.e., unchangeable, such as logs or measure information. Records are added to this table through data streams or chunks. Records are never updated and are removed after they cease to hold any insights. 

The more dynamic factless fact table lacks measures and captures the interconnection between dimensions. Factless tables can be used to track relations between the members of disparate dimensions without a measured value. That these dimensions are interconnected constitutes a fact. Such a table can be used to track business initiatives that lack distinct KPIs, such as incentives, sales-customer relations, and attendance. 

  1. What is a perspective and have you created one? 

Creating a perspective helps reduce the complexity of multiple data blocks. Perspectives are created by shrouding components and focus on a particular set to yield key insights. Perspectives allow businesses to pick and choose what data they can share with prospective clients. In a hospital, patients can be given a data perspective on clinical facilities, culled from aggregated retail and clinic information.

A Few Suggestions Before Your Interview

Before you appear for the interview, review your knowledge of SSAS: interviewers will ask you about SSAS to gauge your proficiency as a developer. Use the answers given above as a base to add nuance based on your own experience. You can also widen your knowledge of SSAS if some of these questions or answers are unfamiliar to you. 

The best site for learning more about SSAS is Microsoft’s documentation page. Get started with this overview

Also read Top 12+ Senior Java Developer Interview Questions and Answers

SSAS Developer Interview Questions You Will be Asked [Updated]

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