Worksheets2025-26-1S-COGNATES3-IST3-SFEXAM
Total questions: 100
Worksheet time: 17mins
Name
Class
Date
1.
A slowly changing dimension (SCD) Type 2 captures __________ history.
a)
a. Current only
b)
b. Full
c)
c. No
d)
d. Random
2.
A role-playing dimension allows one dimension to serve __________ roles.
a)
a. One
b)
b. Multiple
c)
c. Static
d)
d. None
3.
A junk dimension groups __________ attributes into one table.
a)
a. Numeric
b)
b. Miscellaneous
c)
c. Historical
d)
d. Time
4.
Degenerate dimensions are stored in the __________ table.
a)
a. Dimension
b)
b. Fact
c)
c. Log
d)
d. Metadata
5.
A mini-dimension is used to handle __________ attributes.
a)
a. Static
b)
b. Rapidly changing
c)
c. Numeric
d)
d. Random
6.
Conformed dimensions are shared across __________ fact tables.
a)
a. One
b)
b. Multiple
c)
c. Zero
d)
d. Static
7.
A snowflake schema normalizes __________ tables.
a)
a. Fact
b)
b. Dimension
c)
c. Log
d)
d. Cache
8.
A bridge table is often used to manage __________ relationships.
a)
a. One-to-one
b)
b. Many-to-many
c)
c. Random
d)
d. Cache
9.
A factless fact table contains __________.
a)
a. Measures only
b)
b. Keys without measures
c)
c. Dimensions only
d)
d. Metadata
10.
A Type 1 SCD replaces old values with __________ values.
a)
a. Random
b)
b. New
c)
c. Empty
d)
d. Cached
11.
Role-playing dimensions are common in __________ analysis.
a)
a. Static
b)
b. Time
c)
c. Random
d)
d. Firewall
12.
A junk dimension reduces the number of __________ in fact tables.
a)
a. Measures
b)
b. Foreign keys
c)
c. Logs
d)
d. Aggregations
13.
Mini-dimensions improve performance by isolating __________.
a)
a. Static data
b)
b. Frequently changing attributes
c)
c. Foreign keys
d)
d. Random measures
14.
Factless fact tables often capture __________ events.
a)
a. Random
b)
b. Occurrence of activities
c)
c. Metadata
d)
d. Cache
15.
Advanced dimensional design ensures scalability and __________.
a)
a. Randomization
b)
b. Flexibility
c)
c. Metadata errors
d)
d. Static logs
16.
A snowflake schema is less normalized than a star schema.
a)
True
b)
False
17.
Conformed dimensions can be reused across multiple fact tables.
a)
True
b)
False
18.
Junk dimensions store numeric measures.
a)
True
b)
False
19.
A Type 2 SCD maintains historical versions.
a)
True
b)
False
20.
Mini-dimensions handle stable attributes.
a)
True
b)
False
21.
Factless fact tables contain only foreign keys.
a)
True
b)
False
22.
Bridge tables are used for one-to-one relationships.
a)
True
b)
False
23.
Degenerate dimensions exist in fact tables.
a)
True
b)
False
24.
A Type 1 SCD overwrites old values without history.
a)
True
b)
False
25.
Advanced design helps manage complex business models.
a)
True
b)
False
26.
MDX stands for __________.
a)
a. Multi Data Extraction
b)
b. Multidimensional Expressions
c)
c. Memory Data Exchange
d)
d. Metadata Xchange
27.
The SELECT clause in MDX specifies __________.
a)
a. Logs
b)
b. Axes (rows/columns)
c)
c. Metadata
d)
d. Errors
28.
The WHERE clause in MDX acts like a __________ filter.
a)
a. Metadata
b)
b. Slicer
c)
c. Log
d)
d. Network
29.
Calculated members are created using __________.
a)
a. SQL
b)
b. MDX expressions
c)
c. Log files
d)
d. Firewall
30.
A cell in an OLAP cube is defined by its __________.
a)
a. Cache
b)
b. Dimension coordinates
c)
c. Metadata logs
d)
d. Errors
31.
An MDX query returns __________ sets of data.
a)
a. Logs
b)
b. Multidimensional
c)
c. Static
d)
d. Random
32.
The CROSSJOIN function in MDX combines two __________.
a)
a. Errors
b)
b. Sets
c)
c. Logs
d)
d. Keys
33.
A named set in MDX represents a predefined __________.
a)
a. Log
b)
b. Group of members
c)
c. Fact table
d)
d. Firewall rule
34.
The AXIS function in MDX defines __________ placement.
a)
a. Keys
b)
b. Dimensions
c)
c. Metadata logs
d)
d. Random events
35.
A tuple in MDX is a unique combination of __________.
a)
a. Keys
b)
b. Members from different dimensions
c)
c. Logs
d)
d. Aggregations
36.
Hierarchies in MDX allow drill-down and __________.
a)
a. Errors
b)
b. Roll-up
c)
c. Randomization
d)
d. Deletion
37.
MDX queries are optimized for __________ databases.
a)
a. Flat-file
b)
b. Multidimensional
c)
c. Static logs
d)
d. Binary
38.
A cube browser retrieves data through __________ queries.
a)
a. SQL only
b)
b. MDX
c)
c. XML
d)
d. Logs
39.
The NON EMPTY keyword in MDX removes __________.
a)
a. Metadata
b)
b. Null results
c)
c. Foreign keys
d)
d. Errors
40.
Subcubes in MDX restrict the __________.
a)
a. Metadata logs
b)
b. Scope of analysis
c)
c. Keys
d)
d. Errors
41.
MDX is used for multidimensional queries.
a)
True
b)
False
42.
Tuples in MDX represent single members only.
a)
True
b)
False
43.
The CROSSJOIN function combines multiple sets.
a)
True
b)
False
44.
A cube browser cannot execute MDX queries.
a)
True
b)
False
45.
NON EMPTY filters remove null values from results.
a)
True
b)
False
46.
The WHERE clause in MDX acts as a slicer.
a)
True
b)
False
47.
Named sets in MDX cannot be reused.
a)
True
b)
False
48.
Subcubes define smaller portions of cubes for analysis.
a)
True
b)
False
49.
MDX queries cannot create calculated members.
a)
True
b)
False
50.
MDX queries can be used in Excel pivot tables.
a)
True
b)
False
51.
Data mining is the process of discovering __________.
a)
a. Logs
b)
b. Patterns and knowledge
c)
c. Metadata
d)
d. Random errors
52.
A common goal of data mining is __________ prediction.
a)
a. Random
b)
b. Future
c)
c. Static
d)
d. Error
53.
Classification in data mining assigns data to __________.
a)
a. Logs
b)
b. Predefined categories
c)
c. Errors
d)
d. Random events
54.
Clustering groups data into __________.
a)
a. Logs
b)
b. Natural groupings
c)
c. Random keys
d)
d. Metadata
55.
Association analysis identifies __________.
a)
a. Errors
b)
b. Co-occurring items
c)
c. Logs
d)
d. Random metadata
56.
Regression is used for __________ prediction.
a)
a. Binary only
b)
b. Continuous value
c)
c. Error logs
d)
d. Static
57.
Anomaly detection identifies __________.
a)
a. Common trends
b)
b. Outliers
c)
c. Logs
d)
d. Random keys
58.
Decision trees are useful for __________ problems.
a)
a. Errors
b)
b. Classification
c)
c. Logs
d)
d. Random
59.
Market basket analysis is an example of __________ rules.
a)
a. Logs
b)
b. Association
c)
c. Metadata
d)
d. Random
60.
Cross-validation ensures __________ models.
a)
a. Random
b)
b. Reliable
c)
c. Static
d)
d. Error
61.
Data mining requires __________ data quality.
a)
a. Poor
b)
b. Irrelevant
c)
c. High
d)
d. Random
62.
Supervised learning relies on __________ labels.
a)
a. Errors
b)
b. Known
c)
c. Logs
d)
d. Random
63.
Unsupervised learning works with __________ labels.
a)
a. Known
b)
b. No
c)
c. Partial
d)
d. Random
64.
A confusion matrix evaluates __________ models.
a)
a. Logs
b)
b. Classification
c)
c. Regression
d)
d. Random
65.
Lift in data mining measures __________.
a)
a. Errors
b)
b. Improvement over random chance
c)
c. Metadata
d)
d. Logs
66.
Data mining only focuses on historical data.
a)
True
b)
False
67.
Clustering is a supervised learning technique.
a)
True
b)
False
68.
Classification assigns items to predefined groups.
a)
True
b)
False
69.
Association analysis finds frequent patterns.
a)
True
b)
False
70.
Regression is used for predicting continuous values.
a)
True
b)
False
71.
Anomaly detection identifies rare events.
a)
True
b)
False
72.
Market basket analysis is unrelated to association rules.
a)
True
b)
False
73.
Cross-validation improves model robustness.
a)
True
b)
False
74.
Decision trees can be used for regression problems.
a)
True
b)
False
75.
High data quality improves mining accuracy.
a)
True
b)
False
76.
A data mining tool must support __________ data sources.
a)
a. Random
b)
b. Multiple
c)
c. Single
d)
d. Static
77.
Weka is an open-source tool for __________.
a)
a. Logs
b)
b. Machine learning
c)
c. Metadata
d)
d. Errors
78.
RapidMiner provides a __________ interface for workflows.
a)
a. Code-only
b)
b. Visual
c)
c. Log
d)
d. Static
79.
Microsoft SQL Server supports mining via __________ services.
a)
a. SSIS
b)
b. Analysis
c)
c. Log
d)
d. Proxy
80.
Orange is a tool that supports __________ workflows.
a)
a. Random
b)
b. Visual programming
c)
c. Logs
d)
d. Metadata
81.
KNIME is often used for __________ integration.
a)
a. Static
b)
b. Data and analytics
c)
c. Logs
d)
d. Firewall
82.
IBM SPSS Modeler specializes in __________.
a)
a. Errors
b)
b. Predictive analytics
c)
c. Metadata
d)
d. Logs
83.
Data mining tools often include __________ techniques.
a)
a. Logs
b)
b. Classification, clustering, regression
c)
c. Random
d)
d. Metadata
84.
Visualization in tools helps users __________ results.
a)
a. Ignore
b)
b. Interpret
c)
c. Randomize
d)
d. Delete
85.
API support in data mining tools allows __________ integration.
a)
a. Hardware
b)
b. Application
c)
c. Random
d)
d. Log
86.
Open-source mining tools are typically __________ cost.
a)
a. High
b)
b. Low/Free
c)
c. Random
d)
d. Static
87.
Enterprise tools like SAS are known for __________ support.
a)
a. Metadata
b)
b. Scalability
c)
c. Random
d)
d. Log
88.
Data preprocessing features handle missing __________.
a)
a. Logs
b)
b. Values
c)
c. Keys
d)
d. Errors
89.
Tool interoperability ensures compatibility with different __________.
a)
a. Logs
b)
b. Systems
c)
c. Random
d)
d. Static
90.
Cloud-based mining tools provide __________ accessibility.
a)
a. Local only
b)
b. Anywhere
c)
c. Random
d)
d. Error
91.
Weka is an enterprise-only paid mining tool.
a)
True
b)
False
92.
RapidMiner supports visual workflows.
a)
True
b)
False
93.
Orange is a Python-based visual mining tool.
a)
True
b)
False
94.
KNIME integrates both data and analytics.
a)
True
b)
False
95.
Microsoft supports mining via SSRS.
a)
True
b)
False
96.
API integration allows automation across applications.
a)
True
b)
False
97.
SPSS Modeler focuses on descriptive analysis only.
a)
True
b)
False
98.
Data preprocessing improves tool accuracy.
a)
True
b)
False
99.
Enterprise tools are always cheaper than open-source.
a)
True
b)
False
100.
Cloud tools improve accessibility for global teams.
a)
True
b)
False
100 %
