If you are looking for Cognos Interview Questions And Answers For Experienced or Freshers, you are in the right place.
Types of SQL Interview Questions
Here Mindmajix sharing a list of 60 real-time Cognos interview questions and answers, Cognos framework, and Cognos report studio. These questions will help you to crack your next job interview and acquire a dream career as a Cognos developer.
|Types of Cognos Interview Questions|
Here are frequently asked Cognos Interview Questions and Answers, let's have a look into them.
|Tableau Desktop||IBM Cognos|
|Tableau Desktop provides a feature to import and visualize huge data sets, enables users to drill down into the data, and also allows them to make queries.||IBM Cognos makes it easy for users to dynamically explore data relations by transforming data into metadata.|
|Tableau Desktop makes it very easy to use and makes it very comfortable for users who are not explicitly data experts to explore the necessary data without any issues.||IBM Cognos is a wholesome platform for data experts alone as it is not that easy simple enough for users with average skills to explore and learn it.|
|Tableau Desktop works on both Windows and Macintosh OS environments and also has a web-based version||Microstrategy works only on Windows, Linux, and Macintosh OS environments|
|Tableau Desktop is targeted to be used in Small, Medium to Larger enterprises||IBM Cognos is targeted to be used in Small, Medium, and Larger enterprises as well.|
Dynamic Cubes are in-memory OLAP cubes that load data directly from relational data sources that are structured in a star or snowflake schema
IBM Cognos Dynamic Cube consists of the following elements, they are:
|Learn how to use Cognos, from beginner basics to advanced techniques, with online video tutorials taught by industry experts. Enroll for Free Cognos Training Demo!|
Data warehouse with star or snowflake schema
Cognos Dynamic Cubes support various databases.
Supported Databases (in the current 10.2 release) include IBM DB2, IBM Netezza, Microsoft SQL Server, Oracle, Teradata.
|TM1 Cube||PowerPlay Cube||Dynamic Cube|
|In-memory cube technology with writeback support||File-based cube technology||Provides extensive in-memory caching for performance|
|Is optimal for write-back, what-if analysis, planning, and budgeting, or other specialized applications||Interactive analysis experience to a large number of users||Is optimal for read-only reporting and analytics|
|Star or snowflake data structure is not required||The Data source is an operational or transactional system. Do not require star or snowflake data structure||Star or snowflake schema is required|
|Aggregation occurs on demand||File-based cube with preaggregation||Supports in-memory aggregation|
Dynamic cube are in-memory OLAP containers that reside within the DQM server
First model your cube definition in Cognos Cube Designer
|Related Article: Cognos Tutorial for Beginners|
Administration tasks include assigning the cube to the QueryService instance, starting it, monitoring its health, and refreshing its contents.
Aggregate Advisor is a performance optimization utility
Cognos Dynamic Cube supports two types of pre-computed aggregate values:
Only enough memory that is required to hold the defined aggregates is used.
Example: 90 MB can hold the aggregates for gosldw_sales, and the aggregate cache size is set to 1 GB, only 90 MB of memory is used. Over time, if the underlying fact tables grow, the aggregates are allowed to grow to the specified maximum of 1 GB.
Should not use more than 30 GB for the aggregate cache.
All of the following criteria must be met in order to have what is considered to be a durable model:
If the model is not durable, then the following changes must be done to make the model durable:
All new content developed with IBM Cognos BI will be durable, as long as the model is durable. Existing non-durable content can be made durable by appropriately replacing the non-durable components of those specifications with durable components. Existing content is generally made durable over time as the content gets updated for reasons other than durability
For creating security features in cubes we can use the user class views and dimension views.
Dimension views help in hiding certain data from the cube depending on what view you are using.
Then we can use the user class views to the cubes for making that data available for a certain role or group.
We can also use password protection as another way of providing security to the cubes.
It may have 300MB; 400MB depends on the position, Optimum size for a cube is up to 2GB;
A user class is a group of users who need access to the same data and have the same access privileges. The administrator creates the catalog and user classes. Other people in an organization may also create and maintain user classes for the employees in their own department or area.
You can add user classes to a catalog if you created the catalog or you are working with a personal copy of a distributed catalog and your administrator gave you the privilege to add and modify user classes.
Framework Manager is a tool that allows you to make data sources known for use within the various Cognos suites and allows you to add additional logic and details. Within a logical Cognos architecture, a Framework Manager model contains the logic and information required for one of the Cognos tools to use a data source.
IBM Cognos Framework Manager is a metadata modeling tool that drives query generation for IBM Cognos software. A model is a collection of metadata that includes physical information and business information for one or more data sources. IBM Cognos software enables performance management on normalized and denormalized relational data sources and a variety of OLAP data sources.
The Framework Manager model consists of three layers:
The physical, or database, the layer contains a database query subject for every table in the physical data model. The database layer also contains alias shortcuts, which behave as if they were a copy of the original object with completely independent behavior.
The logical layer contains query subjects that draw data from the database query subjects and present it in a more consumable format.
The dimensional layer contains the hierarchies and measure dimensions for publication to a package. Each dimension in the logical layer has a dimension in the dimensional layer with one or more hierarchies defined. The hierarchies usually include the caption field twice, once as a caption for the level, once as an attribute that can be used in report filters. All hierarchies are sorted.
No security is defined for the IBM Cognos Framework Manager model other than the provision for filtering by the tenant_id parameter on the physical layer. These query subject filters can be converted to security filters that are based on user IDs, allowing multi-tenant access to one database.
The IBM Predictive Maintenance and Quality reports use IBM Cognos Compatible Query Mode, which is the supported mode for all of the reports.
With IBM Predictive Maintenance and Quality, you can monitor, analyze, and report on information that is gathered from devices. In addition, recommendations for actions can be generated by Predictive Maintenance and Quality.
The orchestration is the process that ties activities in IBM Predictive Maintenance and Quality together.
Message flows: Orchestration is achieved with message flows in IBM Integration Bus.
Example of an orchestration XML file: An example file, inspection.xml, demonstrates the purpose and structure of an orchestration file.
Generic batch orchestration: Generic batch orchestration provides capabilities to run a scheduler flow and invoke any IBM SPSS batch job by taking inputs from a configurable XML file instead of developing separate message flows for a specific use case.
The orchestration is achieved with message flows in IBM Integration Bus.
The following activities can be tied together:
Message flows are supplied with IBM Predictive Maintenance and Quality and must be customized with IBM Integration Bus. The message flows are organized into the following applications:
Cardinality is used by the query engine to:
Identify query subjects that behave as facts and dimensions
1..n cardinality implies fact data on the n side and dimension data on the 1 side
Avoid double counting fact data
Support loop joins in star schema models
Cardinality is applied in the context of a query
Query subjects may be facts or dimensions depending on the other query subjects included in the query
Determinants Feature first introduced in Cognos 8 used to provide control over granularity when aggregating
When do I need to use determinants:
Use Model Advisor – Check early and often
Model Advisor is a useful tool for detecting common modeling issues, Cardinality issues, Potential join path conflicts, Incorrect determinants. Easy to configure to check as for as much or as little as desired, Run after import to check for areas that need further investigation, Run again periodically to identify potential issues as you work, Direct links to the problem area, and Help.
Dimensionally modeled relational (DMR) constructs in Classic Query Mode
Regular dimensions created from model query subjects or database query subjects
Create measure dimension
By default, Framework Manager will use Cognos SQL to create Query Subjects
In Classic Query Mode, at runtime, native SQL optimized for the data source is generated and passed down through to the data source
Copy of the query engine local in the Framework Manager install
In Dynamic Query Mode, requests are sent to the Query Service for processing
Framework Manager will include passing the necessary elements in the model to Query Service on the server
Use minimized SQL when possible
Certain conditions will trigger as view SQL and override this setting:
“As view” SQL not always bad
A new Dynamic Query Processing Mode (DQM) to the existing query service designed to improve query performance.
Framework Manager 1.x, 8.x, and 10.x includes a full copy of the data access stack
With the introduction of 10.1.1, Framework Manager enables users to work in Dynamic Query mode or Compatible mode
Working in dynamic mode will leverage the always leverage the query service on the server
The compatible mode will continue to leverage the local query components
New metadata import mechanism available for DQM
A new mechanism to test objects and evaluate expressions
On-demand evaluation of expressions in the editor
Can work in compatible mode and enable dynamic query mode for packages
Can switch an existing model to the dynamic mode
Some differences between DMR and OLAP over Relational
Null suppression capabilities
Null handling in the calculation
IBM Cognos Package or Report. You can import query items, and the associated filters and prompts, from IBM Cognos packages and reports. You do this by choosing the Package or Report data source type and browsing and selecting from the available metadata.
Cognos Impromptu is an intuitive, user-friendly system that enables non-technical personnel to quickly and easily design and distribute business intelligence reports.
IBM Cognos TM1 (formerly Applix TM1, formerly Sniper TM/1) is enterprise planning software used to implement collaborative planning, budgeting, and forecasting solutions, as well as analytical and reporting applications.
IBM Cognos Controller is part of an integrated Financial Close Management (FCM) solution, built on an efficient, purpose-built platform. It helps you deliver complete financial results, create financial and management reports, and provide the CFO with an enterprise view of key ratios and metrics.
Flexible enterprise software for planning, budgeting, forecasting, and analysis. IBM Cognos Planning enables you to develop plans, budgets, and forecasts faster and more efficiently. Cognos Planning lets you create, compare, and evaluate business scenarios.
IBM Cognos TM1 is a solution with a client-server architecture and TM1 Architect is one of the standard TM1 client components that can connect to the TM1 server. The key components of TM1 are TM1 Architect, TM1 Perspectives, TM1 Web, and Turbo Integrator.
TM1 Perspectives is a standard TM1 standalone application and needs Microsoft Excel to run. It uses Cube Viewer features while taking advantage of MS Excel functionality via an add-in for Excel.
IBM Cognos TM1 Web uses a multi-tiered architecture that enables users to access and interact with Cognos TM1 data using any supported web browser. The IBM Cognos TM1 Web multi-tiered architecture includes web client, web application server, and data component tiers.
The extension for the framework manager project file is a store in the file system with extension .cpf ( Cognos Project File )
A model serves as an insulating layer between IBM Cognos BI reporting users and the database. Packages are model subsets that ensure users are provided with data appropriate for the reporting they need to do, and that the data is structured in ways that make sense from a business perspective.
Creating models and publishing packages are tasks that should be planned carefully. Models and packages that are well-designed from the start ensure that user requirements are met, data is secure, and your IBM Cognos BI application can be easily administered.
To understand the modeling and packaging process, users can study the sample models, packages, and reports provided with IBM Cognos BI. For information about setting up the samples, see the IBM Cognos Administration and Security Guide.
For IBM Cognos BI reporting, models and packages are created using Framework Manager. The following topics provide an overview. For more information, see the IBM Cognos Framework Manager User Guide.
In IBM Cognos Framework Manager, security is a way of restricting access to metadata and data across IBM Cognos products.
There are different types of security in Framework Manager:
Loop joins in the model are typically a source of unpredictable behavior. This does not include star schema loop joins.
When cardinality clearly identifies facts and dimensions, IBM Cognos software can automatically resolve loop joins that are caused by star schema data when you have multiple fact tables joined to a common set of dimension tables.
In the case of loop joins, ambiguously defined query subjects are the primary sign of problems. When query subjects are ambiguously defined and are part of a loop join, the joins used in a given query are decided based on a number of factors, such as the location of relationships, the number of segments in join paths, and, if all else is equal, the alphabetically first join path. This creates confusion for your users and we recommend that you model to clearly identify the join paths.
Star and snowflake schema designs are mechanisms to separate facts and dimensions into separate tables. Snowflake schemas further separate the different levels of a hierarchy into separate tables.
Star schemas - A star schema is a type of relational database schema that is composed of a single, central fact table surrounded by dimension tables.
Snowflake schemas - The snowflake schema, sometimes called snowflake join schema consists of one Fact table connected to many dimension tables, which can be connected to other dimension tables.
|Related Article: Snowflake Tutorial|
IBM DB2 Warehouse Cubing Services is designed to provide a multidimensional view of data stored in a relational database. With Cubing Services, you can create, edit, import, export, and deploy cube models over the relational warehouse schema. Cubing Services also provides optimization techniques to dramatically improve the performance of OLAP queries, a core component of data warehousing and analytics.
A fact table is a table in a star or snowflake schema that stores facts that measure the business, such as sales, cost of goods, or profit. Fact tables also contain foreign keys to the dimension tables. These foreign keys relate each row of data in the fact table to its corresponding dimensions and levels.
Cubing Services also supports fact tables that are logical, meaning that the fact table of a facts object in a cube model can be a view.
A dimension table is a table in a star or snowflake schema that stores attributes that describe aspects of a dimension. For example, a time table stores the various aspects of time such as year, quarter, month, and day. A foreign key of a fact table references the primary key in a dimension table in a many-to-one relationship.
The DB2 Warehouse cube model represents a logical star schema or snowflake schema and groups relevant dimension objects around a central facts object.
Each dimension can have multiple hierarchies. The structural information about how to join the tables that are used by the facts object and dimensions is referenced by the cube model. Also stored in the cube model is enough information to construct SQL queries and to retrieve OLAP data. Other reporting and OLAP tools that understand the cube model and that can display multiple views of a specific dimension can benefit from using the cube model.
A database is comprised of one or more tables, and the relationships among all the tables in the database are collectively called the database schema. Although there are many different schema designs, databases used for querying historical data are usually set up with dimensional schema design, typically a star schema or a snowflake schema.
There are many historical and practical reasons for dimensional schemas, but the reason for their growth in popularity for decision support relational databases is driven by two main benefits:
Stay updated with our newsletter, packed with Tutorials, Interview Questions, How-to's, Tips & Tricks, Latest Trends & Updates, and more ➤ Straight to your inbox!
|IBM Cognos Training||May 23 to Jun 07|
|IBM Cognos Training||May 28 to Jun 12|
|IBM Cognos Training||May 30 to Jun 14|
|IBM Cognos Training||Jun 04 to Jun 19|
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
Copyright © 2013 - 2022 MindMajix Technologies