DMDW viva Questions

Data Warehousing and Data mining Interview Questions with answers

DMDW viva Questions

1. Which of the following forms the logical subset of the complete data warehouse?
(a)Dimensional model
(b)Fact table
(c)Dimensional table
(d)Operational Data Store
(e)Data Mart.
2.Which of the following is not included in Modeling Applications?
(a)Forecasting models
(b)Behavior scoring models
(c)Allocation models
(d)Data mining Models
(e)Metadata driven models.
3.Which of the following is a dimension that means the same thing with every possible fact table to
which it can be joined?
(a)Permissible snowflaking
(b)Confirmed Dimensions
(c)Degenerate dimensions
(d)Junk Dimensions
(e)Monster Dimensions.
4.Which of the following is not the managing issue in the modeling process?
(a)Content of primary units column
(b)Document each candidate data source
(c)Do regions report to zones
(d)Walk through business scenarios
(e)Ensure that the transaction edit flat is used for analysis.
5.Which of the following criteria is not used for selecting the data sources?
(a)Data Accessibility
(c)Data accuracy
(d)Longevity of the feed
(e)Project scheduling.
6.Which of the following does not relate to the data modeling tool?
(a)Link to the dimension table designs
(b)Business user Documentation
(c)Helps assure consistency in naming
(d)Length of the logical column.
(e)Generates physical object DDL.
7.Which of the following is true on building a Matrix for Data warehouse bus architecture?
(a)Data marts as columns and dimensions as rows
(b)Dimensions as rows and facts as columns
(c)Data marts as rows and dimensions as columns
(d)Data marts as rows and facts as columns
(e)Facts as rows and data marts as columns.
8.Which of the following should not be considered for each dimension attribute?
(a)Attribute name
(b)Rapid changing dimension policy
(c)Attribute definition
(d)Sample data
9.Which of following form the set of data created to support a specific short lived business situation?
(a)Personal Data Marts
(b)Application Models
(c)Downstream systems
(d)Disposable Data Marts
(e)Data mining models.
10.Which of the following does not form future access services?
(b)Report linking
(c)Push toward centralized services
(d)Vendor consolidation
(e)Web based customer access.
11.What is the special kind of clustering that identifies events or transactions that occur
(a)Affinity grouping
12.Of the following team members, who do not form audience for Data warehousing?
(a)Data architects
(c)Business Intelligence experts
13.The precalculated summary values are called as
14.OLAP stands for
(a)Online Analytical Processing
(b)Online Attribute Processing
(c)Online Assertion Processing
(d)Online Association Processing
(e)Online Allocation Processing.
15.Which of the following employ data mining techniques to analyze the intent of a user query,
provided additional generalized or associated information relevant to the query?
(a)Iceberg Query Method
(b)Data Analyzer
(c)Intelligent Query answering
(e)Query Parser.
16.Of the following clustering algorithm what is the method which initially creates a hierarchical
decomposition of the given set of data objects?
(a)Partitioning Method
(b)Hierarchical Method
(c)Density-based method
(d)Grid-based Method
(e)Model-based Method.
17.Which one of the following can be performed using the attribute-oriented induction in a manner
similar to concept characterization?
(a)Analytical characterization
(b)Concept Description.
(c)OLAP based approach
(d)Concept Comparison
(e)Data Mining.
18.Which one of the following is an efficient association rule mining algorithm that explores the levelwise
(a)FP-tree algorithm
(b)Apriori Algorithm
(c)Level-based Algorithm
(d)Partitioning Algorithm
(e)Base Algorithm.
19.What allows users to focus the search for rules by providing metarules and additional mining
(a)Correlation rule mining
(b)Multilevel Association rule mining
(c)Single level Association rule mining
(d)Constraint based rule mining
(e)Association rule mining.
20.Which of the following can be used in describing central tendency and data description from the
descriptive statistics point of view?
(a)Concept measures
(b)Statistical measures
21.Which of the following is the collection of data objects that are similar to one another within the
same group?
(e)Data source.
22.In which of the following binning strategy, each bin has approximately the same number of tuples
assigned to it?
(a)Equiwidth binning
(b)Equidepth binning
(c)Homogeneity-based binning
(d)Equilength binning
(e)Frequent predicate set.
23.Which of the following binning strategy has the interval size of each bin the same?
(a)Equiwidth binning
(b)Ordinary binning
(c)Heterogeneity-based binning
(d)Un-Equaling binning
(e)Predicate Set.
24.Which of the following association shows relationships between discrete objects?
(c)Single Dimensional
25.What algorithms attempt to improve accuracy by removing tree branches reflecting noise in the
(d)FP tree
26.Which of the following process includes data cleaning, data integration, data selection, data
transformation, data mining, pattern evolution, and knowledge presentation?
(a)KDD Process
(b)ETL Process
(c)KTL Process
(d)MDX process
27.What is the target physical machine on which the data warehouse is organized and stored for
direct querying by end users, report writers, and other applications?
(a)Presentation server
(b)Application server
(c)Database server
(d)Interface server
(e)Data staging server.
28.Which of the following cannot form a category of queries?
(a)Simple constraints
(b)Correlated subqueries
(c)Simple behavioral queries
(d)Derived Behavioral queries
(e)Clustering queries.
29.Which of the following is not related to dimension table attributes?
(c)Equally unavailable
30.Type 1: Overwriting the dimension record, thereby loosing the history, Type 2: Create a new
additional dimension record using a new value of the surrogate key and Type 3: Create an old field
in the dimension record to store the immediate previous attribute value. Belong to:
(a)Slow changing Dimensions
(b)Rapidly changing Dimensions
(c)Artificial Dimensions
(d)Degenerate Dimensions

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