Q1) What are the process involved in the datawarehousing project cycle? a) Requirement gathering b) Performance tuning c) Reporting d) ETL(extraction,transformation & loading) Ans. a & b Exp: 0-2 Q2) What is Requirement gathering? Ans. Requirement gathering is associated with identification of user requirements such as hardware sizing information, training requirements, data source identification and a concrete project plan indicating the finish date of the data warehousing project. Exp: 0-2 Q3) What is JAD? Ans. JAD stands for the join application development sessions where multiple people are talking about the project scope in the same meeting. Exp: 0-2 Q4) The approach that we use to decide which business intelligence tool to use are: a) Buy v/s Build b) Available budget c) Time d) Professional services Ans. a Exp: 0-2 Q5) To decide which approach among buy v/s build has to be choosen for selecting the business intelligence tool depends upon: a) User technical skills b) Requirements c) Tool vendor’s stability d) Support Ans: a & b. Exp: 0-2 Q6) Explain the factors that are necessary for the database/hardware selection? Ans: 1) Scalability: Which RDBMS and hardware plat form can handle large sets of data most
efficiently? To get an idea of this, one needs to det ermine the approximate amou nt of data that is to be kept in the dat a warehous e system once it's mature, and base any testing numbers from there. 2) Parallel Processing Support: The days of multi-million dollar supercomputers with one single CPU are gone, and nowadays the most powerful computers all use multiple CP Us, where each processor can perform a part of the task, all at the same time. Indeed, parallel computing is gaining popularity now, although a little slower than I had originally thought.
3) RDBMS/Hardware Combination: Because the RDBMS physically sits on the hardware platform, there are going to be certain parts of the code that is hardware platform -dependent. As a result, bugs and bug fixes are often hardware dependent.
Q7) What are the popular relational databases? Ans. Oracle, Microsoft SQL server, IB M DB2, Teradata, Sybase, MySQL
Q8) Which are the most commonly used business intelligence tools? Ans. Excel, Reporting tool, OLAP tool, Data mining tool.
Q9) Which business tool provide flexibility in terms of each user to create, shedule & run their own reports? a) Excel b) Reporting tool c) OLAP tool d) Data mining tool Ans. b
Exp: 0-2 Q.10) Which tool is used by the users to look at the data from the multiple dimensions?
a) b) c) d) Ans. c Excel Reporting tool OLAP tool Data mining tool
Q.11) What is modeling technique used in datawarehousing? Ans. Dimensional data modeling is commonly used for datawarehousing
Q12) What are the different levels of abstraction for a data model? Ans. Conceptual,logical & Physical level
Q13) What is OLAP and also discuss its types? Ans. OLAP stands for online analytical processing. This enables the people on the business side to analyze the metrics in different dimensions. Types of OLAP: MOLAP: In MOLAP data is stored in multidimensional cube. The storage is not relational database. ROLAP: This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality. In es sence, each action of slicing and dicing is equivalent to adding a "WHERE" clause in the SQL statement. HOLAP: HOLAP technologies attempt to combine the advantages of MOLAP and ROLAP. For summary-type information, HOLAP leverages cube technology for faster performance. When detail information is needed, HOLAP can "drill through" from the cube into the underlying relational data.
Exp: 0-2 Q14) Dimensional data model is used for a) non-transactional type system b) transactional type system c) OLTP type system d) none of the above Ans: b and c Q15) What are the name of the tools in which people put the data modeling information? Ans. ERWin and Oracle Designer Exp: 0-2 Q16) What are the criteria for evaluating OLAP vendors? Ans. The criteria for evaluating OLAP vendors are: Ability to leverage parallelism supplied by RDBMS and hardware: This would greatly increase the tool's performance, and help loading the data into the cubes as quickly as possible. Performance: In addition to leveraging parallelism, the tool itself should be quick both in terms of loading the data into the cube and reading the data from the cube. Metadata support: Because OLAP tools aggregates the data into the cube and sometimes serves as the front-end tool, it is essential that it works with the metadata strategy/tool you have selected. Exp: 0-2