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Test your basic knowledge |
Data Mining
Start Test
Study First
Subject
:
it-skills
Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
study here
.
Match each statement with the correct term.
Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.
This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. Useful for assessing performance in terms of identifying the most important class. Helps such choices as: How many tax records to examine; How many loans to grant; How many customers to mail an offer
transformation mapping
lift charts
knowledge data discovery
aggregate
2. A common example of the use of association methods where a retailer can mine the data generated by a point-of-sale system - such as the price scanner you are familiar with at the grocery store is referred to as:
groves law
market basket analysis
system catalog
measuring predictive error
3. Gives us an idea of the magnitude of errors. Actual value - estimated value.
cascading delete
MAE (Mean Absolute Error) deviation
artificial Key
knowledge data discovery
4. In general - ______________ are transformed to relations/tables by defining one relation for the object itself and another relation for each multivalued attribute.
MOLAP
Scope creep
composite semantic objects
aggregate
5. The process by which numerical data is converted into graphical images is referred to as:
average error
DROP TABLE Salesrep;
data visualization
dimension
6. The set of activities used to find new - hidden - or unexpected patterns in data is referred to as _____.
data visualization
knowledge data discovery
data mining
lift charts
7. Which of the following database design and data warehouse design approaches is viewed to take a more strategic rather than operational perspective?
UNION
Top-down approach
drill-across report
volatile data
8. __________ occurs when the initial scope of a project continues to expand as new features are incorporated into the project.
the relationship
machine learning
The degree of granularity
Scope creep
9. Semantic object link (SOL) attributes establish a relationship between one _______ and another.
PRIMARY KEY (CustomerNum)
measuring predictive error
semantic object
numeric prediction
10. ___________ is not a characteristic of a data warehouse.
PRIMARY KEY (CustomerNum)
drill-across report
volatile data
association semantic object
11. Which function calculates the number of entries in a table?
the relationship
semantic object (SOL) attribute
Count
Top-down approach
12. Which function should be used to calculate the total of all entries in a given column?
Sum
database administrator
semantic object
Cartesian
13. The process that records how data from operational data stores and external sources are transformed on the way into the warehouse is referred to as ________________.
Breakeven analysis
measuring predictive error
transformation mapping
performance metrics - Numeric Prediction
14. The ACCESS feature that tests to see if your tables are normalized properly is the ____.
UNION
Document Analyzer
OLAP
near-line secondary storage devices
15. Which data mining technique utilizes linkage analysis to search operational transactions for patterns with a high probability of repetition?
The degree of granularity
database administrator
Association
n
16. A powerful trend in IT is known as - which maintains that Computer transmission speed doubles every 18 months.
surrogate key
Document Analyzer
Referential integrity
groves law
17. Are a data mining technology.
market basket analysis
OLAP
knowledge data discovery
neural networks & Decision Trees
18. Within most organizations - the person known as the _____ determines the type of access various users can have to the corporate or enterprise database.
Top-down approach
database administrator
dimension
neural networks & Decision Trees
19. Models that do ___________: MLR; KNN; Regression and Classification Trees; ANN; SVM
numeric prediction
Sum
Top-down approach
Group By
20. The minimum cardinality and m is the maximum cardinality Cardinalities in Semantic Objects are shown as subscripts in the format n-m where _____
groves law
n
knowledge data discovery
Association
21. ___________________ is used to relate one set of outcomes (dependent variable) to a set of predictor (independent) variables (e.g. - in time series analysis). Through this analysis we attempt to predictive future events - as the dependent variables b
Regression analysis
cascading delete
Transformation
changing/UPDATE-ing
22. The term "ETL" in data warehousing stands for: Extraction - ________________________ - & Loading.
PRIMARY KEY (CustomerNum)
UNION
neural networks & Decision Trees
Transformation
23. Twice as likely to identify the important class (compared to avg. prevalence)
Sum
Into
decile chart
Regression analysis
24. Which rule would you be violating - if you tried to delete a sales rep record - who currently has customers on file?
lift charts
Insert
principle component analysis
Referential integrity
25. Why are Star Schemas so useful in Financial Planning and Accounting Information Systems?
artificial Key
degrees of summarization
Document Analyzer
dimension
26. The deletion of a record that also deletes related records is referred to as a(n) _____.
project readiness assessment factor
transformation mapping
cascading delete
dimension
27. An economic feasibility measure. So is Internal rate of return.
semantic object (SOL) attribute
Breakeven analysis
maximum
semantic object
28. 'Signatures' are used for intrusion detection by _______?
recognizing known patterns
Breakeven analysis
MOLAP
project readiness assessment factor
29. To add a new row to a table - use the _____ command.
database administrator
Insert
Cartesian
principle component analysis
30. Which clause would be used to create groups of records?
average error
transformation mapping
artificial Key
Group By
31. Which statement removes the table Salesrep from a DBMS?
DROP TABLE Salesrep;
data mining
performance metrics - Numeric Prediction
Top-down approach
32. Organizes and analyzes data as an n-dimensional cube. The cube can be thought of as a common spreadsheet with two extensions: (1) support for multiple dimensions and (2) support for multiple concurrent users.
Breakeven analysis
numeric prediction
MOLAP
DROP TABLE Salesrep;
33. The SQL command for deleting the Warehouse field from the Part table is _____.
Fact or Measurement table
volatile data
ALTER TABLE Part DELETE Warehouse;
semantic object (SOL) attribute
34. R- squared(and adjusted r-squared) - A measure of how much of the variability around the target mean is explained by your predictive variables. Doesn't mean you have a good predictive model—only validation will tell you that
performance metrics - Numeric Prediction
dimension
Group By
Breakeven analysis
35. Not the same as goodness-of-fit; We want to know how well the model predicts new data - not how well it fits the data it was trained with; Key component of most measures is difference between actual y and predicted y (error)
performance metrics - Numeric Prediction
Association
Document Analyzer
measuring predictive error
36. Increased affordability of ____________ is a reason for the growth in popularity of data mining.
Horizontal integration
Revoke
machine learning
Count
37. An analytical-oriented organizational structure is a data warehouse _____________.
average error
neural networks & Decision Trees
OLAP
project readiness assessment factor
38. The SQL built-in functions - which may appear on the same line as the SELECT statement (before the FROM clause) are called _____ functions.
PRIMARY KEY (CustomerNum)
data mining
Cartesian
aggregate
39. A _____________ is a system-generated primary key.
Sum
surrogate key
composite semantic objects
data mart
40. These are considered an alternate storage techniques for data warehousing include.
Association
knowledge data discovery
near-line secondary storage devices
Count
41. Which of the following is at the center of a star schema?
Document Analyzer
Fact or Measurement table
n
knowledge data discovery
42. The term _____ has been generally agreed to represent the broadest category of software technology that enables decision makers to conduct many dimensional analysis of consolidated enterprise data.
OLAP
Scope creep
aggregate
DROP TABLE Salesrep;
43. Which statement will take away user privileges to the database?
data mart
Top-down approach
dimension
Revoke
44. A single column that you create for an entity to serve as the primary key - because you otherwise would need many concatenated columns to do so - is called a(n) ____________.
artificial Key
composite semantic objects
Top-down approach
Group By
45. The _______________________ represents the source data for the DW. This layer is comprised - primarily - of operational transaction processing systems and external secondary databases.
data visualization
operational and external layer
OLAP
DROP TABLE Salesrep;
46. Gives an idea of systematic over- or under-prediction. Magnitude of average absolute error.
average error
The degree of granularity
PRIMARY KEY (CustomerNum)
the relationship
47. An alternative to the data warehouse concept is a lower-cost - scaled-down version referred to as the _____________.
maximum
degrees of summarization
data mart
artificial Key
48. On an ER Diagram the number (mark) on relationship line that is farthest away from each entity (rectangle) represents the _______ cardinality.
Count
neural networks & Decision Trees
maximum
volatile data
49. A synonym for data mining
Insert
Transformation
Sum
knowledge data discovery
50. You can save the results of a query as a table by including the _____ clause in the query.
Into
recognizing known patterns
surrogate key
drill-across report