Top 45+ Practice SSAS Interview Questions [ANSWERED] in 2020
SSAS Interview Questions and Answers

Top 45+ Practice SSAS Interview Questions [ANSWERED]

Last updated on 04th Jul 2020, Blog, Interview Questions

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These SSAS Interview Questions have been designed specially to get you acquainted with the nature of questions you may encounter during your interview for the subject of SSAS.As per my experience good interviewers hardly plan to ask any particular question during your interview, normally questions start with some basic concept of the subject and later they continue based on further discussion and what you answer.we are going to cover top 100 SSAS Interview questions along with their detailed answers. We will be covering SSAS scenario based interview questions, SSAS interview questions for freshers as well as SSAS interview questions and answers for experienced. 

1.What is Analysis Services for SQL Server (SSAS)?


SQL Server Analysis Services (SSAS) is a component of Microsoft SQL Server designed for online analytical processing (OLAP) and data mining. It supports both multidimensional and tabular models, providing flexibility for business intelligence solutions. SSAS enables interactive data analysis, data mining, and integrates with other Microsoft BI tools like Excel, Power BI, and SSRS. It offers scalability, security features, and efficient cube design and processing for optimal performance. SSAS contributes to creating a comprehensive BI ecosystem for organizations to derive insights and make informed decisions.

2.What is the Data Mining Architecture and Techniques explained? Data Mining Architecture in SaaS?


Data Mining Architecture in SaaS :

  • Cloud-Based Infrastructure: Hosts data mining processes on cloud platforms.
  • Data Storage: Utilizes cloud-based databases or warehouses for scalable storage.
  • SaaS Data Mining Service: Offers core data mining capabilities as a service.
  • Web-Based Interface:Allows users to interact through a web-based interface.

Data Mining Techniques in SaaS :

  • Classification, Clustering, Regression: Assigns categories, groups similar data, and predicts numerical values.
  • Association Rule Mining: Identifies relationships between variables.
  • Anomaly Detection:Detects outliers or unusual patterns.
  • Text Mining:Extracts insights from unstructured text data.

Data minining architechture

3.Describe UDM. What role does it play in SSAS?


Unified Dimensional Model (UDM) in SSAS:

Abstraction Layer :

Simplifies interaction between users and underlying data structures.

Business Logic :

Allows inclusion of business logic and calculations in the model.

Data Security :

Supports role-based security for data access control.

Aggregation and Optimization :

Enables pre-calculated aggregations for improved query performance.

Integration with BI Tools :

Seamlessly integrates with BI tools like Excel and Power BI.

    4.Why is the SSAS component necessary?


    Multidimensional Analysis : Enables analysis through cubes.

    OLAP Capabilities : Supports interactive data exploration.

    Data Mining : Includes algorithms for pattern discovery.

    Scalability : Designed for scalable and performant queries.

    Metadata Management : Centralized metadata for consistency.

    Role-Based Security : Ensures controlled data access.

    BI Tools Integration : Seamlessly integrates with tools like Excel.

    Cube Design : Supports efficient multidimensional analysis.

    5.What does SSAS’s TWO-Tier Architecture mean?


    In SSAS’s Two-Tier Architecture, client applications (e.g., Excel) directly communicate with the SSAS server. There is no intermediary layer between them. This simplifies the architecture and can enhance performance, making it suitable for scenarios where direct communication meets the application’s needs, such as Excel connecting directly to SSAS for PivotTable creatio

      6.Which elements make up SSAS?


      The key elements of SQL Server Analysis Services (SSAS) include:

      SSAS Database : Core repository for multidimensional models.

      Cube : Represents multidimensional data for analysis.

      Dimension : Defines data categorization and hierarchies.

      Measure : Quantitative data for analysis.

      Hierarchy : Organizes data within dimensions.

      Data Source : External repository providing data.

      7.Describe OLAP. What distinguishes it from OLTP?


      OLAP :

      • OLAP for analytical queries
      • It uses a multidimensional model
      • It handles complex queries
      • It deals with large volumes of historical and aggregated data

      OLTP :

      • OLTP for transactions.
      • It follows a relational database
      • It manages routine transactions.
      • It prioritizes high transaction concurrency.

      8.What does mean by data source?


      A data source is the origin or repository providing data for applications or analytical processes. It can be a database, data warehouse, file, web service, or API. Connecting to a data source involves using specific methods or protocols. In analytics, data from various sources may undergo ETL processes for integration. The quality of data from the source impacts the accuracy of analysis. Examples include customer databases, spreadsheets, web services, and log files. Managing data sources is crucial for effective data management and analytics.

          9.Does Impersonation Mean?


          Impersonation involves assuming the identity or privileges of another user for specific actions or resource access. It is commonly used for delegated authority, authentication, and authorization. Implementation should be secure, managing credentials and ensuring proper authorization. In Windows, it allows a process to temporarily adopt another user’s security attributes. Impersonation is prevalent in programming and APIs for specific operations. Security considerations include auditability to track actions performed during impersonation.

          10.What does mean by data source view?


          A Data Source View (DSV) is a logical representation of data sources in tools like SQL Server Analysis Services (SSAS). It simplifies complex data structures, allowing users to design multidimensional models without directly interacting with source complexities. DSVs integrate with business intelligence tools for report creation and contribute to centralized metadata management. In SSAS, DSVs are foundational for defining dimensions, measures, and cubes in multidimensional models.

          11.What is a Role in SSAS, and how is it used for security?


          In SSAS (SQL Server Analysis Services), a Role is a security mechanism that assigns specific permissions to users or groups. Roles control access to cubes and dimensions, and they can be applied at various levels, including dimension data and individual cells. Roles use MDX expressions to define dynamic security rules, allowing for conditional access based on user-specific criteria. This feature ensures that users only see authorized data, enhancing security and governance in the SSAS environment.

          12.Explain the role of the SSAS Storage Engine?


          The SSAS Storage Engine (SQL Server Analysis Services) is in charge of managing data storage, retrieval, and optimization in multidimensional or tabular models. It retrieves data from the source, employs caching for performance, handles aggregations, compression, and manages data partitioning. The Storage Engine collaborates with the Formula Engine for efficient query execution and calculation processing, contributing to the overall performance of SSAS.

          13.How does SSAS handle dynamic security, and why is it important?


          SSAS (SQL Server Analysis Services) implements dynamic security through MDX expressions, allowing the creation of security rules that adapt based on user-specific conditions. This approach enables conditional access to data, often associated with specific roles. Dynamic security is crucial for enforcing business rules, adapting to changes in user attributes, and providing fine-grained control over data access. It enhances confidentiality by ensuring users only see information relevant to their roles, contributing to a flexible and personalized security model within SSAS.

          14.Describe the purpose of the SSAS Partition?


          The SSAS (SQL Server Analysis Services) Partition serves the purpose of optimizing query performance and managing the storage and processing of data within a cube. It allows for the logical division of data into subsets, facilitating parallel processing, efficient data retrieval, and storage optimization. Partitions support incremental processing, enabling the update of specific data subsets without processing the entire cube. This results in improved query performance, scalability, and the ability to customize processing logic for different subsets of data. Overall, SSAS Partitions contribute to creating an efficient and responsive analytical environment.

            15.What is the significance of the SSAS Attribute Hierarchy?


            The SSAS (SQL Server Analysis Services) Attribute Hierarchy is significant for organizing and structuring data attributes within dimensions. It enables efficient querying, supports drill-down and roll-up operations, and enhances the user experience in multidimensional analysis and reporting by providing a natural and intuitive way to explore data at various levels of granularity.

            16.How can you implement row-level security in SSAS?


            To implement row-level security in SSAS:

            • Define roles representing user groups.
            • Assign users to roles (Windows or database roles).
            • Create DAX expressions to filter data based on user attributes.
            • Apply these filters to tables or views in the SSAS data model.
            • Test to ensure users see only data allowed by their security rules.

            17.What are the benefits of using the SSAS Dimension Wizard?


            The SSAS Dimension Wizard streamlines dimension creation in a multidimensional model with a user-friendly interface, reducing development time and ensuring consistency. It automates attribute setup, hierarchy definition, and relationship establishment. The wizard supports customization while providing a preview feature for validation, enhancing efficiency and minimizing errors in the dimension design process.

            18.How does SSAS handle semi-additive measures?


            “Drillthrough” in SSAS refers to the capability of accessing detailed data behind summarized values in a cube. Users can drill through from aggregated data to see the underlying detailed information, providing a more granular view for analysis.

            19.What is the purpose of the SSAS Attribute Key?


            The SSAS Attribute Key uniquely identifies attributes within a dimension, aiding in efficient data retrieval, relationships between dimensions, and ensuring data integrity in the SSAS multidimensional model.

            20.What is a named set in SSAS, and how is it used?


            A Named Set in SSAS is a custom subset of dimension members defined using MDX expressions. It enables users to reference and reuse specific data subsets in queries, calculations, or reports, promoting code modularity. By enhancing analysis, Named Sets offer a flexible way to create subsets based on specific criteria or conditions within a multidimensional model.

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              21.Explain the concept of SSAS Perspectives and when they might be beneficial?


              SSAS Perspectives are logical views or subsets of a cube’s metadata that simplify the presentation of dimensions, hierarchies, and measures. They are beneficial when a cube has a complex structure, and different user groups require a customized view of the cube’s components. Perspectives enhance user experience by providing focused and relevant information.

              22.How can the performance of SSAS calculations be improved?


              To optimize the performance of SSAS calculations:

              • Use efficient MDX expressions to minimize calculation time.
              • Leverage server resources by offloading calculations to the server.
              • Employ aggregation design to precalculate and store aggregated values.
              • Limit the use of calculated members for frequently used calculations.

              23.What is the role of the SSAS Deployment Wizard?


              The SSAS (SQL Server Analysis Services) Deployment Wizard plays a crucial role in deploying SSAS projects from development to production environments. It facilitates the packaging, validation, and deployment of SSAS databases, ensuring a smooth migration process. The wizard assists in managing configurations, security settings, and other deployment-related tasks, ensuring that the deployed SSAS solution aligns with the intended production environment.

              24.How does SSAS support the integration of Key Performance Indicators (KPIs)?


              SSAS supports the integration of Key Performance Indicators (KPIs) by providing a dedicated KPI object within the cube structure. Developers define KPIs with components such as value expression, goal expression, status expression, and trend expression. These KPIs can be seamlessly integrated with reporting tools, enabling real-time monitoring and analysis of key business metrics. The dynamic nature of KPIs in SSAS allows for automatic recalculation as data changes, providing flexibility and customization to suit specific organizational requirements.

              25.Explain the difference between a calculated column and a calculated measure in SSAS?


              A calculated column is a column added to a table in the data source view and computed based on expressions or formulas. It becomes part of the physical data structure. A calculated measure, on the other hand, is a dynamic calculation created within a cube using MDX expressions. It is calculated at query time and does not physically exist in the data source.

              26.What is the purpose of the SSAS Usage-Based Optimization Wizard?


              The SSAS Usage-Based Optimization Wizard analyzes query patterns and usage statistics to recommend aggregations that can enhance cube performance. It assists in optimizing the cube’s design by suggesting efficient aggregations based on actual usage patterns.

              27.How do you troubleshoot performance issues in an SSAS cube?


              Troubleshooting performance issues involves monitoring query execution, analyzing cube design, using SQL Server Profiler to capture queries, reviewing aggregation design, optimizing MDX calculations, and ensuring that indexing and partitioning are configured appropriately.

              28.Describe the concept of Role-Playing Dimensions in SSAS?


              Role-playing dimensions occur when a single dimension is used multiple times in a cube with different roles. For example, a “Date” dimension may be used for “Order Date” and “Ship Date.” Role-playing dimensions enable the representation of different perspectives on the same dimension within a cube.

              29.How can you implement incremental cube processing in SSAS?


              Incremental cube processing involves updating only the data that has changed since the last processing. This can be achieved by using partitioning, processing only the affected partitions, or by utilizing the “Process Add” option for dimensions to add new members.

              30.What is the purpose of the SSAS Aggregation Design?


              The SSAS Aggregation Design is used to precalculate and store aggregated values to enhance query performance in a cube. It involves defining aggregations at different levels to optimize query response times.

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              31.Explain the concept of SSAS Data Mining.


              SSAS Data Mining involves using data mining algorithms to discover patterns, trends, and insights within large datasets. It allows users to build predictive models and make data-driven decisions.

              SSAS Data Mining

              32.How does SSAS handle slowly changing dimensions (SCDs)?


              SSAS handles slowly changing dimensions by supporting various SCD types through dimension properties. This enables the management of historical changes in dimension attributes.

                33.What is the purpose of the SSAS Query Log?


                The SSAS Query Log records information about queries executed against the cube. It aids in performance analysis, troubleshooting, and identifying usage patterns to optimize cube design.

                34.Explain the concept of SSAS Perspectives and when they might be beneficial?


                SSAS Perspectives are logical views of a cube’s metadata that simplify the presentation of dimensions and measures. They are beneficial when different user groups require customized views to focus on specific aspects of the cube.

                35.How can you improve the processing performance of an SSAS cube?


                Improving processing performance involves strategies such as optimizing dimension processing, partitioning large tables, using incremental processing, and optimizing indexes. Aggregation design and proper cube design also contribute to better processing performance.

                36.What is a Factless Fact Table, and when might it be used in a data warehouse?


                A Factless Fact Table is a table in a data warehouse that contains only keys and no measures. It is used to represent events or occurrences where the fact of interest is the existence of the event rather than a numerical measure.

                Factless Fact Table

                37.Explain the concept of SSAS Actions.


                SSAS Actions allow users to associate custom operations or URLs with specific cube elements. Actions can be triggered when interacting with data in a client tool, enabling users to navigate to related information or perform custom operations.

                  38What is the purpose of the SSAS Cache, and how can you optimize its performance?


                  The SSAS Cache stores aggregated and calculated results to improve query response times. Performance can be optimized by configuring cache settings, ensuring proper memory allocation, and managing cache expiration policies.

                  39.Explain the concept of Writeback in SSAS?


                  Writeback in SSAS allows users to update or write back data to the cube. It’s useful for scenarios where users need to input adjustments or planning data directly into the cube, affecting subsequent analyses.

                    40.How can you monitor SSAS performance, and what tools are available for this purpose?


                    Monitor SSAS performance using SQL Server Profiler for queries, Perfmon Counters for resource metrics, and SSAS Management Studio for server activity. Utilize SQL Server Data Tools for cube optimization and third-party tools for enhanced monitoring. Windows Event Viewer captures service events, while custom logging allows tailored tracking of user interactions and business events.

                    41.Describe the concept of SSAS Perspectives and when they might be beneficial?


                    SSAS Perspectives are logical views of a cube’s metadata, beneficial when different user groups require customized views. They simplify the cube’s structure for specific users without altering the actual cube design.

                      42.What is the role of the SSAS MDX (Multidimensional Expressions) language?


                      Multidimensional databases use the SSAS MDX (Multidimensional Expressions) language as their query language. It facilitates filtering, member navigation, computations, and data retrieval. Flexible multidimensional data querying and manipulation are made possible by MDX’s time-based analysis and set operations features. For creating queries, carrying out computations, and modifying cube behaviour inside SSAS multidimensional models, its expressive syntax is essential.

                        43.Explain the concept of Parent-Child Hierarchies in SSAS?


                        In SSAS (SQL Server Analysis Services), a Parent-Child Hierarchy is a hierarchical structure where each member in a dimension has a direct relationship with another member as its parent or child. Unlike regular hierarchies, which have predefined levels, Parent-Child Hierarchies can have a variable depth. This type of hierarchy is often used to represent organizational structures, such as reporting relationships in an employee hierarchy or bill of materials in a product structure. Each member is associated with a parent member, except for the top-level member, which has no parent. Parent-Child Hierarchies are defined using specific attributes that establish the parent-child relationships within a dimension.

                         44.How does SSAS handle security, and what are the different security roles available?


                        SSAS provides role-based security, where users are assigned to roles with specific permissions. Roles include Administrator, Database Administrator, Cube Administrator, Data Reader, and Data Writer, allowing fine-grained control over access.

                          45.What is the role of the SSAS Storage Engine?


                          The physical storage of data within a cube is managed by the SSAS (SQL Server Analysis Services) Storage Engine. It is in charge of putting data structures in order, handling aggregations, running queries, applying compression, storing frequently accessed data in cache, using indexing, guaranteeing concurrency control, and facilitating data partitioning for improved effectiveness and performance.

                          46.How can you implement row-level security in SSAS?


                          Row-level security in SSAS is implemented using security roles and MDX expressions. Security roles are defined with filters based on user attributes, restricting data access at the row level.

                            47.Describe the purpose of the SSAS Usage-Based Optimization Wizard?


                            The SSAS Usage-Based Optimization Wizard analyzes query patterns and usage statistics to recommend aggregations that can enhance cube performance. It assists in optimizing the cube’s design based on actual usage.

                            48.Explain the concept of the SSAS Proactive Caching feature?


                            Pre-aggregating and storing aggregations during off-peak hours is known as proactive caching in SSAS, and it guarantees that users receive faster query response times during periods of high demand..

                            49.What is a Cube in SSAS?


                            In SSAS, a cube is a multidimensional structure facilitating efficient data analysis. It organizes data along dimensions like time or geography, with measures representing analyzed numerical data. Cubes enable comprehensive exploration and reporting, enhancing business intelligence capabilities.

                            SSAS CUBE

                            50.What is the purpose of the SSAS Deployment Wizard?


                            SSAS projects are deployed from development to production environments using the SSAS Deployment Wizard. It facilitates the packaging, deployment, and validation of SSAS databases, guaranteeing a seamless migration procedure.

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                              51.What is the purpose of the SSAS Proactive Caching feature, and when might it be beneficial?


                              The purpose of the SSAS Proactive Caching feature is to optimize cube performance by pre-aggregating and storing aggregations during non-peak hours. This process anticipates user queries and ensures that aggregated results are readily available, reducing the need for on-the-fly computations during query execution.

                              52.How can you implement row-level security in SSAS?


                              Row-level security in SSAS is implemented using security roles. Security roles are defined with filters based on user attributes, restricting data access at the row level and ensuring users only see authorized data.

                              53.How does SSAS handle slowly changing dimensions (SCDs), and what are the common types of SCDs?


                              SSAS handles slowly changing dimensions by supporting various SCD types, including Type 1 (overwrite), Type 2 (add new row), and Type 3 (add new column). These types define how historical changes in dimension attributes are managed.

                                54.What are Measures in SSAS?


                                Measures are the quantitative values that users want to analyze. Examples include sales, revenue, or quantity. Measures are typically stored in fact tables in a data warehouse.

                                55.What is the role of the SSAS Storage Engine in cube processing?


                                In addition to handling aggregations and ensuring effective retrieval during cube processing and query execution, the SSAS Storage Engine also oversees the physical storage of data.

                                56.Explain the concept of a calculated measure in SSAS?


                                In SSAS (SQL Server Analysis Services), a calculated measure is a user-defined metric created through MDX expressions. These expressions use existing measures to perform mathematical operations, implement conditional logic, or derive custom business-specific metrics, enhancing analytical capabilities within a cube.

                                57.How does SSAS handle cube processing tasks, and what is the significance of processing options?


                                SSAS handles cube processing tasks such as dimension processing, partition processing, and aggregations. Processing options define how SSAS updates and refreshes the cube’s data and metadata.

                                  58.Explain the concept of partitioning in SSAS, and how does it contribute to performance optimization?


                                  In SSAS, partitioning entails breaking up cube data into digestible chunks. It helps to optimise performance by facilitating efficient management of large datasets and parallel processing.

                                  59.Describe the role of the SSAS Query Log, and how can it be utilized for performance analysis?


                                  The SSAS Query Log records details about user queries. It can be utilized for performance analysis by reviewing query patterns, identifying frequently accessed data, and optimizing cube design based on actual usage.

                                  60.What is the significance of the SSAS Translation feature, and how can it enhance the user experience?


                                  The SSAS Translation feature allows the cube to support multiple languages for dimension attribute labels. It enhances the user experience by providing a localized view of the cube for users in different language settings. Explain the concept of the SSAS Drillthrough feature and its practical applications.

                                  61.What is MDX, and why is it important in SSAS?


                                  MDX (Multidimensional Expressions) is a query language integral to SQL Server Analysis Services (SSAS). It specializes in retrieving and manipulating data from multidimensional databases, offering a powerful means for querying, analyzing, and applying calculations to complex data structures, such as cubes with dimensions and hierarchies. MDX is crucial for ad hoc analysis, supporting business intelligence efforts, and integrating with reporting tools in the context of SSAS.

                                  62.Name some algorithms used in SSAS for data mining?


                                  Decision Trees, Clustering, and Neural Networks are just a few of the data mining algorithms that SSAS in SQL Server uses for tasks like pattern recognition and classification. Complex dataset scenarios call for the use of algorithms like Naive Bayes and Association Rules, whereas temporal analysis and predictive modelling benefit from the application of Time Series and Linear Regression. When data is divided into clusters using K-Means Clustering, anomalies are found using anomaly detection. The analytical powers of SSAS are improved by these algorithms, which offer insightful information to companies.

                                  63.Explain what aggregations are in SSAS?


                                  Aggregations in SSAS are pre-calculated summaries of data in OLAP cubes. They enhance query performance by storing aggregated results, reducing the need for on-the-fly calculations during queries. These summaries are defined at different levels within the cube’s dimensions, optimizing the trade-off between storage space and query responsiveness.

                                  64.What is a dimension in SSAS?


                                  In SSAS, a dimension is a component that provides context to measures in a cube. It organizes data with descriptive attributes and hierarchies, facilitating meaningful analysis. For example, a Time dimension might have hierarchies like Year > Quarter > Month. Dimensions enhance the structure and understanding of multidimensional data.

                                  65.Explain the difference between a regular dimension and a role-playing dimension?


                                  A regular dimension is used once in a cube, while a role-playing dimension is used multiple times with different roles, such as Date dimension serving as Order Date and Ship Date. Cube processing in SSAS involves updating and refreshing data within a cube. It includes tasks like loading data, building aggregations, and updating dimension members.

                                  66.How do you deploy an SSAS project?


                                  To deploy an SSAS project, open SQL Server Management Studio, connect to the target SSAS server, and use either Visual Studio or SQL Server Data Tools to right-click on the project and choose “Deploy.” Monitor the deployment progress in the Output window and verify success on the SSAS server.

                                  67.Explain the role of Data Source Views in SSAS?


                                  Designers can establish relationships and specify data types for use in the cube by using Data Source Views (DSVs), which in SSAS offer a logical view of the underlying data sources.

                                  68.Can you have multiple data sources in a single SSAS cube?


                                  Certainly! In SSAS, you can integrate data from multiple sources into a single cube, facilitating a unified analytical view. This feature enables the consolidation of diverse data for comprehensive analysis within a single framework. Data source views (DSVs) play a key role in managing and combining information from different databases or sources.

                                  69.Why do we use partitions in SSAS cubes?


                                  Partitions allow for dividing large cubes into smaller, more manageable units. Each partition can be processed independently, enhancing performance and manageability.

                                  70.What is proactive caching in SSAS?


                                  Proactive caching in SSAS allows the cube to pre-aggregate and store query results, ensuring faster response times for frequently requested data.

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                                  71.What is the difference between process update and process full in SSAS?


                                  Process Update refreshes data in the cube without affecting existing data, while Process Full rebuilds the entire cube, removing existing data and recalculating all aggregations.

                                  72.Describe the relationships between fact and dimension tables in both Star and Snowflake Schemas?


                                    Aspect Star Schema Snowflake Schema

                                  Centralized fact table with denormalized dimension tables.

                                  Centralized fact table with normalized dimension tables..
                                  Relationships Direct one-to-many relationships between fact table and dimension tables. Hierarchical relationships between fact table and normalized dimension tables (sub-dimensions).
                                  Complexity Simpler and easier to understand. Generally more denormalized. More complex due to normalization.Requires more joins in queries.

                                    73.How do you filter data in MDX?


                                    To filter data in MDX, use the WHERE clause in your query. For instance, [Time].[Year].&[2022] limits the results to the year 2022. This clause allows you to apply conditions and constraints to focus on specific dimensions or members, tailoring the query results to your analysis needs

                                    74.What is the role of the SELECT statement in MDX?


                                    In MDX, the SELECT statement is crucial for shaping query results. It defines the dimensions and hierarchies to be displayed on columns and rows, setting the context for data analysis. For example, [Measures].[Sales] ON COLUMNS specifies sales data on the columns of the result set. This statement is fundamental for retrieving and presenting data from multidimensional cubes.

                                    75.What is lazy aggregation in SSAS?


                                    Lazy aggregation in SSAS defers the creation of some aggregations until query time, optimizing storage and processing efficiency.

                                    76.How does SSAS handle data mining model training and prediction?


                                    SSAS separates model training and prediction phases, allowing the creation of models using historical data and subsequent use for predicting future outcomes.

                                    77.What is the purpose of the property AttributeHierarchyDisplayFolder?


                                    AttributeHierarchyDisplayFolder: Indicates the folder where end users should see the related attribute hierarchy. For instance, all the attributes of a dimension will be placed into a folder named “Test” if I set the property value of “Test” to all of the attributes of that dimension.

                                    78.What does AttributeHierarchyEnabled mean to use?


                                    AttributeHierarchyEnabled: This variable indicates whether Analysis Services will create an attribute hierarchy for the given attribute. The attribute cannot be used in a user-defined hierarchy or referenced in Multidimensional Expressions (MDX) statements if the attribute hierarchy is not enabled.

                                    79.In SSAS, what is aggregation?


                                    In order to avoid having to recalculate the data from a data source for every query, aggregations refer to the pre-calculated totals that can be obtained straight from the cube.Performance can be enhanced and improved with the use of aggregations. The needs of the business must guide the design of aggregations.The Aggregation Design Wizard is used to plan and execute aggregations.

                                    80.Why and how are translations used?


                                    Translation: You can show attribute names and captions that match a particular language by using the analysis service’s translation feature. It aids in giving the Cube GLOBALISATION.

                                    81.What are the uses of aggregations?


                                    By enabling Microsoft SQL Server Analysis Services (SSAS) to retrieve pre-calculated totals straight from cube storage rather than needing to recalculate data from an underlying data source for every query, aggregations enhance performance.

                                    82.What is UDM? What does it mean in SSAS?


                                    Providing a bridge between the user and the data sources is the function of a Unified Dimensional Model (UDM). After building a UDM over one or more physical data sources, the end user can query the UDM with a variety of client tools, including Microsoft Excel. A simpler, easier-to-understand model of the data, isolation from heterogeneous backend data sources, and better performance for summary type queries are the benefits that the end-user receives when the UDM is built as little more than a thin layer over the data source.

                                    83.What languages does SSAS support?


                                    SQL, or Structured Query Language :

                                    • An industry-standard query language focused on analysis is called Multidimensional Expressions (MDX).
                                    • Data Mining Extensions (DMX) is a query language that is industry standard and focused on data mining.
                                    • The language used to manage Analysis Services database objects is called Analysis Services Scripting Language (ASSL).

                                    84.distinguishes MOLAP, ROLAP, and HOLAP from one another?


                                    MOLAP (Multidimensional OLAP) :

                                    • Data Storage: Multidimensional cube format.
                                    • Performance: Fast query performance due to pre-aggregated data.
                                    • Scalability: May have limitations due to storage requirements.
                                    • ROLAP (Relational OLAP):

                                    • Data Storage: Relational databases.
                                    • Performance: Can be slower for complex queries.
                                    • Scalability: Generally more scalable, leveraging relational databases.
                                    • HOLAP (Hybrid OLAP):

                                    • Data Storage: Combination of MOLAP and ROLAP.
                                    • Performance: Balances between MOLAP and ROLAP, leveraging pre-aggregated data and relational databases.
                                    • Scalability: Provides a compromise between scalability and performance.

                                    MOLAP vs ROLAP vs HOLAP 

                                    85.What is UDM? Describe its importance within the SSAS?


                                    The Unified Dimensional Model in SSAS is referred to as UDM. Between the users and the data sources, it serves as a bridge. It facilitates the gathering of information from all relevant sources into a single model.The Dimensional model, Data Source, and Data Source View are some of the components that make up UDM.

                                    86.Could you briefly describe the steps involved in making a cube?


                                    The procedures to make a cube are listed below :

                                    • establishment of a data source
                                    • The development of the data source view.
                                    • The development of dimensions.
                                    • Making a cube.
                                    • deployment and cube processing.

                                    87.What do you mean by named queries, and what is the purpose of using them?


                                    The SQL expressions or queries in the data source view that will function as a table are known as named queries. Combining data from one or more tables is the named query’s primary function. No schematic modifications to the source data are necessary when using named queries. To verify the data source, use the named query.

                                    88.How can a new column be added to a cube once it has been created?


                                    After the cube is created, a new column can be added using Named calculation. By using either hard coded values, pre existing values, or a combination of the two, a named calculation facilitates the addition of a new column in the data source view.

                                    89.How are aggregations used to optimize query performance in SSAS?


                                    Aggregations in SSAS optimize query performance by precalculating and storing summarized data at different levels within a cube. This reduces the need for calculations during query execution, improving response times, and enhancing storage efficiency by storing compressed, summarized information.

                                    90.Discuss the deployment process in SSAS and the key considerations?


                                    In SSAS, the deployment process involves moving database objects from development to production. Key considerations include choosing between project and traditional deployment methods, using the SSAS Deployment Wizard, and ensuring proper configuration of data sources for seamless deployment and functionality in the production environment.

                                    91.What is the difference between a star schema and a snowflake schema in SSAS?


                                    A star schema has a central fact table connected to dimension tables directly, while a snowflake schema normalizes dimension tables by further dividing them into sub-dimensions.

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