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Data Warehousing And Data Mining
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Course Name
BCA (Bachelor of Computer Application)
Subject Code BC0058 (Data Warehousing And Data Mining)
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PART - A
PART - B
PART - C
Subject : Data Warehousing And Data Mining
BC0058 : PART - A
| Q. 1 | Which of the following architectures involves
multiple processors share common memory and
disk storage and running under a single
operating system? |
| | A) | Symmetric Multi Processing |
| B) | Massively Parallel Processing | | C) | Synchronised Modular Processing | | D) | None of these | | | | | Q. 2 | The normal life cycle of a data mart is
generally |
| | A) | 10 years |
| B) | 5 years | | C) | 3 years | | D) | None of the above | | | | | Q. 3 | Which of the following is not a component
of the data warehouse project team? |
| | A) | Programmer |
| B) | DSS Analyst | | C) | Background Expert | | D) | None of these | | | | | Q. 4 | ____ handles mission-critical production
data accepted through simple queries |
| | A) | OLTP |
| B) | OLAP | | C) | Neural Network | | D) | None of the above | | | | | Q. 5 | The type of data model that is used in a
data warehouse is usually ____ |
| | A) | Multi-dimensional |
| B) | Normalized | | C) | Transactional | | D) | None of these | | | | | Q. 6 | A description of why a project is being done
is the concern of ____ |
| | A) | Project Objective |
| B) | Project Justification | | C) | Project Description | | D) | None of these | | | | | Q. 7 | The type of data model that is used in a
data warehouse is usually ____ |
| | A) | Multi-dimensional |
| B) | Normalized | | C) | Transactional | | D) | None of these | | | | | Q. 8 | Who must have strong organizational and
planning skills |
| | A) | Project Director |
| B) | Chief Executive | | C) | Project Manager | | D) | Data Provision Specialist | | | | | Q. 9 | ____ allows us to understand the
relationship between data |
| | A) | Data Mining |
| B) | OLTP | | C) |
OLAP | | D) | None of the above | | | | | Q. 10 | Training a Neural Network to balance a pole
is part of |
| | A) | Inductive learner |
| B) | Machine learning | | C) | Knowledge discovery in databases (KDD) | | D) | Both A) and B) | | | |
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