A data warehouse includes data from various sources including legacy systems. Legacy systems implies:
Systems that have been developed at different times by different people for a variety of purposes 100.0%
Systems which are no longer useful 0.0%
Systems whose data is outdated 0.0%
Systems whose technology is outdated 0.0%
Systems whose data is corrupt 0.0%
A data warehouse is a "subject-oriented, integrated, time-variant, non-volatile collection of data in support of management's decision-making process". The data within the warehouse is integrated i...
Users from all departments help to create the database 0.0%
It contains the data of the enterprise in its entirety 100.0%
The final product is a fusion of various legacy system information into a cohesive set of information 0.0%
Every user has access to the data in the warehouse 0.0%
Data from various departments is collected into the warehouse 50.0%
It contains the data of the enterprise in its entirety 50.0%
A data warehouse is a "subject-oriented, integrated, time-variant, non-volatile collection of data in support of management's decision-making process". The term non-volatile means that:
The data is refreshed often 100.0%
The data is backed up often 0.0%
The data is deleted often 0.0%
The data is rarely changed 0.0%
The data is of low volume 0.0%
A datawarehouse should be able to implement advanced query functionality. This means :
The RDBMS must provide a complete set of analytic operations including core sequential and statistical operations 100.0%
The RDBMS must not have any architectural limitations 0.0%
The RDBMS server must support hundreds, even thousands, of concurrent users while maintaining acceptable query performance 0.0%
Query performance must not be dependent on the size of the database, but rather on the complexity of the query 0.0%
The warehouse must ensure local consistency, global consistency, and referential integrity 0.0%
A means of extending the data accessible to the end user beyond that which is stored in the OLAP server is know as :
Consolidation 0.0%
Multi Dimensional Analysis 100.0%
Drill Down 0.0%
Navigation 0.0%
Reach through 0.0%
A multi-dimensional data set is sparse if:
The data to be analysed is less in volume 0.0%
If a relatively high percentage of the possible combinations (intersections) of the members from the data set's dimensions contain missing data 100.0%
If a relatively high percentage of the possible combinations (intersections) of the members from the data set's dimensions contain invalid data 0.0%
If a relatively high percentage of the possible combinations (intersections) of the members from the data set's dimensions contain valid data 0.0%
If a relatively high percentage of the possible combinations (intersections) of the members from the data set's dimensions contain outdated data 0.0%
A multidimensional cube records a set of data derived from:
Fact tables 100.0%
Pivot tables 0.0%
Dimensions 0.0%
Fact tables and Dimensions 0.0%
Fact tables and Pivot tables 0.0%
A slice is:
A subset of a multi-dimensional array corresponding to a single value for one or more members of the dimensions not in the subset 100.0%
A subset of a multi-dimensional array corresponding to multiple values for one or more members of the dimensions not in the subset 0.0%
A subset of a multi-dimensional array corresponding to a single value for one or more members of the dimensions in the subset 0.0%
A subset of a multi-dimensional array corresponding to multiple values for one or more members of the dimensions in the subset 0.0%
A subset of a multi-dimensional array not corresponding to a single value for one member of the dimensions not in the subset 0.0%
A Star Schema is a database design that consists of:
A fact table 50.0%
Dimension tables 0.0%
Pivot tables 0.0%
A fact and pivot tables 0.0%
A fact table and one or more dimension tables 50.0%
A structure that stores multi-dimensional information, having one cell for each possible combination of dimensions is known as:
Table 0.0%
Section 100.0%
Partition 0.0%
Cube 0.0%
Repository 0.0%
Changing the view of the data to a greater level of detail is known as:
Explosion 0.0%
Drill down 0.0%
Drill up 100.0%
Exploration 0.0%
Aggregation 0.0%
Changing the view of the data to a higher level of aggregation is known as:
Implosion 0.0%
Drill down 0.0%
Drill up 100.0%
Synchronisation 0.0%
Summarisation 0.0%
Data Mining is also known as
Data Extraction 0.0%
Data Cleansing 50.0%
Data Archiving 0.0%
Knowledge Discovery in Databases (KDD) 50.0%
Data Preservation 0.0%
Data quality management refers to the fact that:
Ad-hoc analysis must not be slowed or inhibited by the performance of the data warehouse RDBMS 0.0%
The warehouse must ensure local consistency, global consistency, and referential integrity 100.0%
The RDBMS server must support hundreds, even thousands, of concurrent users while maintaining acceptable query performance 0.0%
The server must include tools that co-ordinate the movement of subsets of data between warehouses 0.0%
The RDBMS must provide a complete set of analytic operations including core sequential and statistical operations 0.0%
Data Volatility describes:
The degree to which data and data structures change over time 0.0%
The redundancy of the data 0.0%
The volume of the data 0.0%
The compactness of the data 100.0%
The validity of the data 0.0%
Given the following steps between raw data and extracted knowledge, arrange them in the correct order: 1 Data mining 2 Transformation 3 Selection 4 Pre-processing 5 Interpretation and Evaluation
3,4,2,1,5 100.0%
4,3,2,1,5 0.0%
4,2,1,3,5 0.0%
4,1,2,3,5 0.0%
3,4,1,2,5 0.0%
Granularity refers to the:
Validity of the data stored in a data warehouse 100.0%
The level of detail of the facts stored in a data warehouse 0.0%
The timeliness of the data stored in a data warehouse 0.0%
The redundancy of the data stored in a data warehouse 0.0%
Compactness of the data stored in a data warehouse 0.0%
HOLAP stands for:
Hierarchical On-line Analytical Processing 100.0%
Hybrid On-line Analytical Processing 0.0%
Horizontal On-line Analytical Processing 0.0%
Hyper On-line Analytical Processing 0.0%
HyperCube On-line Analytical Processing 0.0%
In a star schema, a table which contains data about one of the dimensions is called a:
Fact table 100.0%
Meta table 0.0%
Data Dictionary 0.0%
Pivot table 0.0%
Dimension table 0.0%
In a star schema, the central table which contains the individual facts being stored in the database is called a:
Fact table 100.0%
Meta table 0.0%
Data Dictionary 0.0%
Pivot table 0.0%
Dimension table 0.0%
In the Discovery model of Data Mining, the emphasis is on which of the following?
The system automatically discovering important information hidden in the data 100.0%
The user who is responsible for formulating the hypothesis and issuing the query on the data to affirm or negate the hypothesis 0.0%
Volume of the data being examined 0.0%
Timeliness of the data 0.0%
Speed with which the data is examined 0.0%
In the Verification model of Data Mining, the emphasis is on which of the following?
The system automatically discovering important information hidden in the data 100.0%
The user who is responsible for formulating the hypothesis and issuing the query on the data to affirm or negate the hypothesis 0.0%
Volume of the data being examined 0.0%
Timeliness of the data 0.0%
Speed with which the data is examined 0.0%
In which component of the enterprise is the data re-organised for analysis and information extracted from the data?
The Data Warehouse 100.0%
The Data Mart 0.0%
The Data Mine 0.0%
The operational RDBMS 0.0%
Metadata 0.0%
Metadata does not include:
The actual data 100.0%
A description of tables and fields in the warehouse, including data types and the range of acceptable values 0.0%
A similar description of tables and fields in the source databases, with a mapping of fields from the source to the warehouse 0.0%
A description of how the data has been transformed, including formulae, formatting, currency conversion, and time aggregation 0.0%
Information that is needed to support and manage the operation of the data warehouse 0.0%
Normalisation is:
The process of organising data in accordance with the rules of a relational database 100.0%
The process of cleansing the data 0.0%
The process of integrating the data into the datawarehouse from legacy systems 0.0%
The process of compressing the data 0.0%
The process of eliminating invalid data before it is introduced into the data warehouse 0.0%
Normalization applied to the dimension tables of a star schema is known as:
Snowflaking 100.0%
Synchronization 0.0%
Slicing and Dicing 0.0%
Replication 0.0%
Data transformation 0.0%
OLAP queries can be characterised as on-line transactions that do not:
Access small amounts of data 100.0%
Analyse the relationships between many types of business elements e.g. sales, products, regions, and channels 0.0%
Compare aggregated data over hierarchical time periods e.g. monthly, quarterly, yearly 0.0%
Present data in different perspectives e.g. sales by region vs. sales by channels by product within each region 0.0%
Respond quickly to user requests, so that users can pursue an analytical thought process without being stymied by the system 0.0%
Replication refers to the:
Physical copying of data from one database to another 100.0%
Cleansing of the data 0.0%
Integration of data from various sources into the data warehouse 0.0%
Analysis of the data 0.0%
Recovery of data 0.0%
ROLAP stands for:
Recyclic On-line Analytical Processing 100.0%
Relational On-line Analytical Processing 0.0%
Reduced On-line Analytical Processing 0.0%
Rotated On-line Analytical Processing 0.0%
Redundant On-line Analytical Processing 0.0%
SQL stands for:
Structured Query Language 100.0%
Systematic Query Language 0.0%
Structured Query Logic 0.0%
Structured Queuing Logic 0.0%
Standard Query Logic 0.0%
The applications of Data Mining would not include:
Discovering buying-patterns for cross selling 100.0%
Financial market prediction 0.0%
Discovering errors made during data entry 0.0%
Discovering which customer is most profitable 0.0%
Credit assessment 0.0%
The data warehouse is typically a large database on a high performance SMP system. Here SMP stands for:
Symmetric Multi-Processing 100.0%
Superior Multi-Processing 0.0%
Systematic Massive Processing 0.0%
Symmetric Massive Processing 0.0%
Systematic Multi-Processing 0.0%
The logical organisation of data in a database is called:
Normalisation 100.0%
Schema 0.0%
View 0.0%
Fact table 0.0%
Dimension 0.0%
The main impetus behind data warehousing was:
To discover means to reduce the data volumes 100.0%
To make OLTP systems work faster 0.0%
To reduce human interaction with database systems 0.0%
To access corporate knowledge repositories based on huge databases to make sound business decisions 0.0%
To standardise the database products used 0.0%
The main objective of Data Mining is:
The safe storage of data 100.0%
Elimination of errors from the data 0.0%
Deleting data that is no longer important to the organization 0.0%
The extraction of implicit, previously unknown, and potentially useful information from data 0.0%
To help in the generation of reports for the management 0.0%
The main objects used by OLAP programs are:
Multidimensional cubes 100.0%
Metadata 0.0%
RDBMS tables 0.0%
Fact tables 0.0%
Pivot tables 0.0%
The Metadata of the data warehouse should at least contain:
The structure of the data 100.0%
The algorithm used for summarisation 0.0%
The mapping from the operational environment to the data warehouse and the algorithm used for summarisation 0.0%
The structure of the data and the algorithm used for summarisation 0.0%
The structure of the data, the algorithm used for summarisation and the mapping from the operational environment to the data warehouse and the algorithm used for summarisation 0.0%
The modification of data as it is moved into the data warehouse is:
Data Transformation 100.0%
Replication 0.0%
Synchronization 0.0%
Data migration 0.0%
Normalization 0.0%
The movement of data from one environment to another is known as:
Data Migration 100.0%
Normalization 0.0%
Replication 0.0%
Data Mining 0.0%
Data Cleansing 0.0%
The requirement that the datawarehouse RDBMS server must support hundreds and thousands of concurrent users while maintaining an acceptable query performance is known as:
Terabyte Scalability 100.0%
Load Performance 0.0%
Mass User Scalability 0.0%
Data Quality Management 0.0%
Query Performance 0.0%
The term OLAP was coined by:
Date 100.0%
Codd 0.0%
IBM 0.0%
Oracle 0.0%
Microsoft 0.0%
Under OLAP terminology, slice and dice refers to:
The user-initiated process of navigating by calling for page displays interactively, through the specification of slices via rotations and drill down/up 100.0%
Restricting the view of database objects to a specified subset 0.0%
A means of extending the data accessible to the end user beyond that which is stored in the OLAP server 0.0%
Computing all of the data relationships for one or more dimensions 0.0%
Applying calculations to input data at the time the request for that data is made 0.0%
Which Data Mining function/technique is used to analyse a collection of records over a period of time?
Classification 100.0%
Associations 0.0%
Sequential/Temporal patterns 0.0%
Clustering 0.0%
Segmentation 0.0%
Which Data Mining technique partitions the database so that each partition or group is similar according to some criteria or metric ?
Clustering and Segmentation 100.0%
Induction 0.0%
Neural Networks 0.0%
Data Visualisation 0.0%
Linear Regression Analysis 0.0%
Which of the following are the modes of OLAP?
MOLAP 50.0%
ROLAP 50.0%
KOLAP 0.0%
Which of the following features are required by OLAP applications?
Multidimensional views of data 100.0%
Calculation-intensive capabilities 0.0%
Time intelligence 0.0%
Multidimensional views of data and Calculation-intensive capabilities 0.0%
Calculation-intensive capabilities and Time intelligence 0.0%
Which of the following is an architecture for OLAP?
MOLAP 100.0%
ROLAP 0.0%
KOLAP 0.0%
MOLAP and ROLAP 0.0%
MOLAP, ROLAP and KOLAP 0.0%
Which of the following is not associated with data warehousing?
Transaction processing 100.0%
Information retrieval and analysis 0.0%
Multi-dimensional data model 0.0%
Query processing 0.0%
Transformed and summarised data 0.0%
Which of the following is not true regarding an OLTP system?
OLTP is generally regarded as unsuitable for data warehousing 100.0%
OLTP systems can be repositories of facts and historical data for business analysis 0.0%
The purpose of an OLTP system is to run day-to-day operations 0.0%
The Data Model of an OLTP system is normalised 0.0%
OLTP offers large amounts of raw data 0.0%
Which of the following is not true regarding the process of Data Mining?
Software techniques are used for finding patterns and regularities in sets of data 100.0%
It is the computer that is responsible for finding the patterns by identifying the underlying rules and features in the data 0.0%
Data mining analysis tends to work from the data up 0.0%
The best techniques are those developed with an orientation towards small volumes of data 0.0%
The analysis process starts with a set of data, uses a methodology to develop an optimal representation of the structure of the data, during which time knowledge is acquired 0.0%
Which of the following queries would be correlated with a Data warehouse?
What is the current account balance of this customer? 100.0%
How many customers have not paid their balances on time? 0.0%
What is the total number of customers in the middle region? 0.0%
Which product line sells best in middle region and how does this correlate to demographic data? 0.0%
Which customer makes the maximum purchases? 0.0%
Which of the following rules would be considered the central core of OLAP?
Multidimensional Conceptual View 100.0%
Intuitive Data Manipulation 0.0%
Accessibility 0.0%
Batch Extraction vs Interpretative 0.0%
Transparency 0.0%
Which of the following stage is concerned with the extraction of patterns from the data?
Selection 100.0%
Pre-processing 0.0%
Transformation 0.0%
Data Mining 0.0%
Interpretation and Evaluation 0.0%
Which of the following statements is incorrect regarding Data Mining?
It is the process of turning data into information 100.0%
It is a collection of many techniques 0.0%
It is a replacement for OLAP 0.0%
It is based on machine generated hypothesis 0.0%
It is used in Decision Support, Prediction, Forecasting and Estimation 0.0%
Which of the following techniques can be used to improve query performance?
Denormalization 100.0%
Partitioning 0.0%
Summarization 0.0%
Denormalization and Partitioning 0.0%
Denormalization, Partitioning and Summarization 0.0%
Which of the following type of data is most likely to be stored on some form of mass storage ?
Metadata 100.0%
Highly summarised data 0.0%
Lightly summarised data 0.0%
Current detail data 0.0%
Older detail data 0.0%
Which of the following would be the only similarity between a datawarehouse and OLTP system?
Purpose 100.0%
Structure of data 0.0%
Type of data 0.0%
Condition of data 0.0%
Data model 0.0%
Which of the following would not be an application of Data Mining in the banking field?
Detect patterns of fraudulent credit card use 100.0%
Ascertaining the number of transactions made in a day 0.0%
Determine credit card spending by customer groups 0.0%
Find hidden correlation between different financial indicators 0.0%
Predict the customers likely to change their credit card affiliation 0.0%
Which of the following would not be considered as a variable affecting the design of an OLAP system?
Query demand 0.0%
Source of data 100.0%
Number of dimensions 0.0%
Atomic data volume 0.0%
Data volatility 0.0%
Which technique of Data Mining involves developing mathematical structures with the ability to learn?
Clustering and Segmentation 0.0%
Neural Networks 100.0%
Fuzzy Logic 0.0%
Linear Regression Analysis 0.0%
Rule based Analysis 0.0%