SUBJECTS
|
BROWSE
|
CAREER CENTER
|
POPULAR
|
JOIN
|
LOGIN
Business Skills
|
Soft Skills
|
Basic Literacy
|
Certifications
About
|
Help
|
Privacy
|
Terms
|
Email
Search
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. _________ seeks to ensure that each application under development is fully integrated within its own boundaries and to eliminate any inconsistencies in the final software product.
data visualization
aggregate
Regression analysis
Horizontal integration
2. A compound semantic object is an object that contains at least one ____.
changing/UPDATE-ing
Scope creep
Revoke
semantic object (SOL) attribute
3. An ___________ relates two other objects.
UNION
system catalog
neural networks & Decision Trees
association semantic object
4. Which clause would be used to create groups of records?
degrees of summarization
Revoke
numeric prediction
Group By
5. Gives us an idea of the magnitude of errors. Actual value - estimated value.
neural networks & Decision Trees
Revoke
MAE (Mean Absolute Error) deviation
Sum
6. ___________________ 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
principle component analysis
Cartesian
Group By
7. ___________ determines exactly what level of detail constitutes a fact record.
drill-across report
The degree of granularity
Regression analysis
Breakeven analysis
8. A ___________ combines result sets from more than one fact table.
neural networks & Decision Trees
average error
drill-across report
transformation mapping
9. Are a data mining technology.
lift charts
neural networks & Decision Trees
Scope creep
The degree of granularity
10. Models that do ___________: MLR; KNN; Regression and Classification Trees; ANN; SVM
numeric prediction
performance metrics - Numeric Prediction
artificial Key
cascading delete
11. To add a new row to a table - use the _____ command.
Insert
transformation mapping
semantic object (SOL) attribute
groves law
12. Which statement will take away user privileges to the database?
Revoke
data visualization
surrogate key
MOLAP
13. Which rule would you be violating - if you tried to delete a sales rep record - who currently has customers on file?
Referential integrity
ERD Modeling
data visualization
MOLAP
14. Twice as likely to identify the important class (compared to avg. prevalence)
the relationship
decile chart
transformation mapping
n
15. The _______________________ represents the source data for the DW. This layer is comprised - primarily - of operational transaction processing systems and external secondary databases.
system catalog
operational and external layer
transformation mapping
groves law
16. The term "ETL" in data warehousing stands for: Extraction - ________________________ - & Loading.
Breakeven analysis
the relationship
data mining
Transformation
17. Gives an idea of systematic over- or under-prediction. Magnitude of average absolute error.
average error
operational and external layer
data visualization
numeric prediction
18. The process by which numerical data is converted into graphical images is referred to as:
drill-across report
data visualization
data mart
changing/UPDATE-ing
19. Which function should be used to calculate the total of all entries in a given column?
data visualization
Sum
OLAP
average error
20. 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.
MOLAP
system catalog
cascading delete
ERD Modeling
21. To create the primary key clause for the Customer table on the CustomerNum field - which of the following is the correct statement?
PRIMARY KEY (CustomerNum)
machine learning
Top-down approach
average error
22. A _____________ is a system-generated primary key.
OLAP
degrees of summarization
surrogate key
Breakeven analysis
23. 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 ________________.
neural networks & Decision Trees
Revoke
transformation mapping
groves law
24. 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) ____________.
principle component analysis
database administrator
artificial Key
semantic object (SOL) attribute
25. In general - ______________ are transformed to relations/tables by defining one relation for the object itself and another relation for each multivalued attribute.
Sum
machine learning
composite semantic objects
lift charts
26. 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.
Regression analysis
near-line secondary storage devices
OLAP
decile chart
27. Which of the following is at the center of a star schema?
system catalog
measuring predictive error
degrees of summarization
Fact or Measurement table
28. Information about tables in the database is kept in the _____.
system catalog
Cartesian
measuring predictive error
recognizing known patterns
29. Semantic object link (SOL) attributes establish a relationship between one _______ and another.
Fact or Measurement table
composite semantic objects
Breakeven analysis
semantic object
30. An analytical-oriented organizational structure is a data warehouse _____________.
project readiness assessment factor
surrogate key
ALTER TABLE Part DELETE Warehouse;
performance metrics - Numeric Prediction
31. A synonym for data mining
operational and external layer
degrees of summarization
artificial Key
knowledge data discovery
32. Why are Star Schemas so useful in Financial Planning and Accounting Information Systems?
project readiness assessment factor
ERD Modeling
artificial Key
degrees of summarization
33. 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
drill-across report
semantic object (SOL) attribute
lift charts
near-line secondary storage devices
34. When an entity has a minimum cardinality of one it means the entity is required in _______.
the relationship
MOLAP
system catalog
Horizontal integration
35. 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:
market basket analysis
performance metrics - Numeric Prediction
Regression analysis
machine learning
36. 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)
UNION
project readiness assessment factor
measuring predictive error
data visualization
37. __________ occurs when the initial scope of a project continues to expand as new features are incorporated into the project.
drill-across report
market basket analysis
Revoke
Scope creep
38. Generally Semantic Object Modeling (SOM) is consideredmore bottom-up oriented than _____________.
ERD Modeling
DROP TABLE Salesrep;
groves law
association semantic object
39. The ACCESS feature that tests to see if your tables are normalized properly is the ____.
ERD Modeling
aggregate
Top-down approach
Document Analyzer
40. 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
n
Regression analysis
machine learning
performance metrics - Numeric Prediction
41. An alternative to the data warehouse concept is a lower-cost - scaled-down version referred to as the _____________.
knowledge data discovery
data mart
market basket analysis
volatile data
42. These are considered an alternate storage techniques for data warehousing include.
near-line secondary storage devices
composite semantic objects
ALTER TABLE Part DELETE Warehouse;
data visualization
43. The _____ operation of two tables results in a single table with the same columns as the first table - and containing all rows that are in the first table merged with all the rows in the second table - minus any duplicate rows.
data visualization
decile chart
near-line secondary storage devices
UNION
44. The SQL command for deleting the Warehouse field from the Part table is _____.
ALTER TABLE Part DELETE Warehouse;
aggregate
the relationship
OLAP
45. ___________ is not a characteristic of a data warehouse.
volatile data
composite semantic objects
Insert
Scope creep
46. The minimum cardinality and m is the maximum cardinality Cardinalities in Semantic Objects are shown as subscripts in the format n-m where _____
Insert
market basket analysis
n
Breakeven analysis
47. The set of activities used to find new - hidden - or unexpected patterns in data is referred to as _____.
surrogate key
MOLAP
OLAP
data mining
48. An economic feasibility measure. So is Internal rate of return.
Horizontal integration
PRIMARY KEY (CustomerNum)
Breakeven analysis
n
49. Increased affordability of ____________ is a reason for the growth in popularity of data mining.
data mart
Fact or Measurement table
dimension
machine learning
50. Which of the following database design and data warehouse design approaches is viewed to take a more strategic rather than operational perspective?
Horizontal integration
machine learning
OLAP
Top-down approach