ERWIN Intermediate Quiz

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ERWIN Quiz

ERWIN Quiz : This ERWIN Intermediate Quiz contains set of 125 ERWIN Quiz which will help to clear any exam which is designed for Intermediate.



1) Which of the following is not true about Junk Dimensions

  1. ans) Helps to have all the very low cardinality attributes in the Fact entity
  2. Designed generally for indicator fields.
  3. Helps Database space optimization
  4. collective set of attributes from Fact

Answer : A

 
 
2) A dimensional table referred in a Fact table multiples times for various Business Attribute is

  1. Conformed Dimension
  2. Outriggered Dimension
  3. Minidimension
  4. ans) Role Playing Dimension

Answer : D

 
 
3) _________  is the term used for how much data we have for a particular dimension/entity of the model.

  1. Data defects
  2. Data validation
  3. Data sparsity

Answer : C

 
 
4) Causal dimensions can be used

  1. As a helper table
  2. For explaining why a record exists in a fact table
  3. For integrating data marts into a data warehouse
  4. For handling changes to the data.

Answer : B

 
 
5) Dimension table may NOT be populated from

  1. Look up data
  2. Transaction data
  3. Master data
  4. Reference Data

Answer : B

 
 
6) Dimension where data quality cannot be guaranteed is

  1. Conformed dimension
  2. Dirty Dimension
  3. Big Dimension
  4. None of the above

Answer : B

 
 
7) Fact table may not contain

  1. non additive Measures
  2. Semi additive measures
  3. Hierarchy
  4. Foreign Keys to Dims

Answer : C

 
 
8) Fact tables that does not contain measures are also called as

  1. Dirty Dimension
  2. BIG dimensions
  3. Factless Fact Table
  4. Semi Additive

Answer : C

 
 
9) From the user query and reporting perspective, which of the following dimension table design will provide better performance?

  1. Denormalized/Flat Dimension with Surrogate keys(integers)
  2. Snowflake Dimension
  3. Denormalized/Flat Dimension with Natural Keys(alphanumeric)
  4. None of the choices

Answer : A

 
 
10) How can a fast changing dimension be handled?

  1. Break off the fast changing attributes into separate dimensions
  2. Similar to slowly changing dimension type 2
  3. Similar to slowly changing dimension type 3
  4. None of the above

Answer : A




 
11) In data distribution the Dense Dimensions

  1. are those for which most of the members do not have a value
  2. are those for which most of the members actually have a data value
  3. are used for Aggregates
  4. are used for exceptional handling.

Answer : B

 
 
12) In what scenario a dimension can be a dependent entity?

  1. Snow flaking
  2. Sub-typing
  3. Both of these
  4. None of these

Answer : C

 
 
13) Logical representation of multidimensional data is called

  1. SCHEMA
  2. CUBE
  3. Relational DB
  4. PDM

Answer : B

 
 
14) Operational Control numbers (e.g. Order Number) are stored in

  1. Separate Dimension table linked to Fact
  2. Separate Dimension table without linked to Fact
  3. Fact Table
  4. Not Stored

Answer : C

 
 
15) Profit Margin is a example of ______ type of fact.

  1. Fully Additive
  2. Semi Additive
  3. Non Additive
  4. None of the above.

Answer : C

 
 
16) Separating few attributes from a Dimension tables that consist of millions of rows is

  1. Mini-dimension
  2. Extended Dimension
  3. Factless Dimension
  4. Junk Dimension

Answer : A

 
 
17) The amount of data we have for a particular dimension/entity of the model is known as

  1. Aggregated Data
  2. Data Sparsity
  3. Factual Data
  4. None of the above.

Answer : B

 
 
18) The following is the characteristic(s) of Aggregations?

  1. Aggregations are precalculated summaries of data
  2. Aggregations are stored in the multidimensional structure in cells at coordinates specified by the dimensions
  3. Results in the fastest possible response time
  4. All the above

Answer : D

 
 
19) What are helper tables?

  1. A degenerate dimension (DD) acts as a dimension key in the fact table, however does not join to a corresponding dimension table because all its interesting attributes have already been placed in other analytic dimensions.
  2. It is a dimension is a convenient grouping of flags and indicators.
  3. It is a dimension that means the same thing with every possible fact table to which it can be joined
  4. It is a table placed between a fact and a dimension table or between two dimension tables so that the dimensions can become multivalued, many to many (M;M) relationships can be resolved.

Answer : D

 
 
20) What is a level of Granularity of a fact table?

  1. level of detail that you put into the fact table in a data warehouse
  2. It describes the amount of space required for a database
  3. indicates the extent of aggregation that will be permitted to take place on the fact data
  4. All the above

Answer : D




 
21) Which  of the following dimension type stores the value of the dimension in fact table instead of the dimension table?

  1. Conformed Dimension
  2. inferred Dimension
  3. Degenerate Dimension
  4. Junk Dimension

Answer : C

 
 
22) Having a Code or Flag value in a large fact table is

  1. Never to be done as a rule
  2. Can be done if a suitable bitmap index is defined
  3. Can be defined using a junk dimension
  4. b & c

Answer : D

 
 
23) If we decide to place a  dimensional attribute as a non-key attribute in a Fact table, what is it called?

  1. Semi-additive Measure
  2. Degenerate Dimension
  3. Fully Additive measure
  4. None of the choices

Answer : B

 
 
24) Fact tables are which of the following?

  1. Completely denormalized
  2. Partially denormalized
  3. Completely normalized
  4. Partially normalized

Answer : C

 
 
25) How do you define a Valid Value for an attribute?

  1. By defining allowable data value(s) for a specified data element.
  2. Allowing any Numeric value
  3. Allowing any alphanumeric value
  4. All of the above

Answer : A

 
 
26) BI Semantic layer will be designed from _________

  1. Conceptual Data Model
  2. Logical Data Model
  3. Subject area data model
  4. None of these

Answer : B

 
 
27) Control / Audit tables are designed during

  1. Conceptual Data Modeling
  2. Logical Data Modeling
  3. Physical Data Modeling
  4. Subject Area Data Modeling phase

Answer : B

 
 
28) What is true about mapping a conceptual model to a logical model?

  1. Defining entities and identifying Primary Keys and other Attributes for each entity
  2. Linking entities with relationships
  3. A & B
  4. None of the above

Answer : C

 
 
29) Entity Relation Model is a

  1. Logical Data Model
  2. Physical Data Model
  3. Functional Data Model
  4. None of the above

Answer : A

 
 
30) Logical Data Model is often used by

  1. Business Users
  2. Designers
  3. Implementors
  4. All of the above

Answer : B




 
31) Maximum modeling effort needs to be on

  1. Conceptual Data Model
  2. Logical Data Model
  3. Physical Data Model
  4. Metadata model

Answer : B

 
 
32) After which stage will you carry out the data staging design and development?

  1. Dimensional modeling
  2. Physical design
  3. Technical architecture design
  4. Requirements definition

Answer : B

 
 
33) Column data type finalization takes place during

  1. Conceptual Data Modeling
  2. Logical Data Modeling
  3. Physical Data Modeling
  4. None of these

Answer : C

 
 
34) Correct attribute / column data type selection is done during

  1. Conceptual Data Modeling
  2. Logical Data Modeling
  3. Physical Data Modeling
  4. None of these

Answer : C

 
 
35) Cross-reference (X-Ref) table design is part of

  1. Conceptual Data Model
  2. Logical data model
  3. Physical data model
  4. None of these

Answer : C

 
 
36) DBAs typically get involved during ____ phase

  1. Conceptual Data Modeling
  2. Logical Data Modeling
  3. Physical Data Modeling
  4. Subject Area Data Modeling phase

Answer : C

 
 
37) How do you define the below scenario/action in physical data model ?
Scenario/action: Each time an instance in the parent entity is deleted, the foreign key
attribute(s) in each related instance in the child entity are set to NULL

  1. By Using Cascade option
  2. By Using Restrict option
  3. By Using Set Null
  4. By Using Set Default

Answer : C

 
 
38) How do you define the below scenario/action in physical data model ?
Scenario/action: Each time an instance in the parent entity is deleted, the foreign
key attribute(s) in each related instance in the child entity are set to the
specified default value.

  1. By Using Cascade option
  2. By Using Restrict option
  3. By Using Set Null
  4. By Using Set Default

Answer : D

 
 
39) How do you define the below scenario/action in physical data model?
Scenario/action: Deletion of an instance in the parent entity is prohibited if there are one or more related instances in the child entity.

  1. By Using Cascade option
  2. By Using Restrict option
  3. By Using Set Null
  4. By Using Set Default

Answer : B

 
 
40) How do you define the below scenario/action in physical data model?
Scenario/action: Each time an instance in the parent entity is deleted, each related instance in the child entity must also be deleted.

  1. By Using Cascade option
  2. By Using Restrict option
  3. By Using Set Null
  4. By Using Set Default

Answer : A




 
41) How do you NOT minimize locking problems caused by uncommitted transactions?

  1. Perform all error handling outside transactions
  2. Keep transactions as short as possible
  3. Do not prompt for user input within a transaction
  4. Avoid recursive nested transactions

Answer : A

 
 
42) IF I want to store true / false in sql values from my frontend which datatype should I use at the best level interms of minimum storage?

  1. Char
  2. Varchar
  3. Bit
  4. Int

Answer : C

 
 
43) In which model do you define database partitioning strategy?

  1. Conceptual Model
  2. Logical Model
  3. Physical Model
  4. None of the above

Answer : C

 
 
44) Physical Data Model include

  1. Convert entities into tables
  2. Convert relationships into foreign keys and Modify the physical data model based on physical constraints / requirements
  3. Convert attributes into columns
  4. All the options (A,B&C)

Answer : D

 
 
45) Staging area data model is typically a

  1. Conceptual data model
  2. Logical Data Model
  3. Physical data model
  4. None of these

Answer : C

 
 
46) The partition by clause in analytical function helps to

  1. Divide the query result
  2. Group the query result
  3. Reset the RANK when group changes
  4. express the value

Answer : C

 
 
47) What trade-offs typically take place while converting from Logical Data Model to Physical Data Model?

  1. Controlled de-normalization
  2. Derived Aggregate table structures
  3. Both of these
  4. None of these

Answer : C

 
 
48) Which data model is Not presented to end business user community?

  1. Conceptual Data Model
  2. Logical Data Model
  3. Physical Data Model
  4. Subject Area data model

Answer : C

 
 
49) Which data model is RDBMS dependent?

  1. Conceptual Data Model
  2. Logical Data Model
  3. Physical Data Model
  4. Subject Area data model

Answer : C

 
 
50) You are in the process of deciding the indexes required for your application DB.
This process belong to which of the following phase ?

  1. Physical design
  2. Logical design
  3. Optimization plan
  4. Fall back plan

Answer : A




 
51) Physical Data Model is often used by

  1. Business Users
  2. Designers
  3. Implementors
  4. All of the above

Answer : C

 
 
52) RDBMS specific features are implemented in

  1. Physical Data Model
  2. Metadata model
  3. Conceptual Data Model
  4. Logical Data Model

Answer : A

 
 
53) Domain Integrity enforces valid entries for a given column by

  1. restricting the Type
  2. restricting the Format
  3. restricting the range of possible values
  4. all of the above

Answer : D

 
 
54) Domain is the set of of possible values of the ————-

  1. attribute
  2. entity
  3. relationship
  4. None of the above

Answer : A

 
 
55) What indicates having the potential to contain more than one value for an attribute at any given time?

  1. Constraint
  2. Single-valued
  3. All of the above
  4. None of the above

Answer : D

 
 
56) What is true for Domain-Based Association?

  1. When Attribute can take multiple values for a single occurance of an entity type.
  2. When attribute can take single value for a single occurance of an enity type.
  3. Domain-Based association pattern is a special use of an association pattern where none of the relationhship is a domain pattern
  4. All of the above.

Answer : A

 
 
57) The Fact is normally in

  1. 1st Normal form
  2. 2nd Normal form
  3. 3rd Normal form
  4. not Normalized at all

Answer : C

 
 
58) When should denormalization would be considered?

  1. Complexity in data retrieval from more tables
  2. database performance is slow
  3. for reporting applications, where a lot of data needs to filtered very often.
  4. All

Answer : D

 
 
59) What is false about Denormalization?

  1. Denormalization is done when there are lot of tables involved in retrieving data.
  2. Denormalization is done in dimensional modelling used to construct a data ware house.
  3. This is not usually done for databases of transactional systems.
  4. Denormalization should never be done

Answer : D

 
 
60) Conformed dimensions are used

  1. For handling multi valued dimensions
  2. For integrating data marts into a data warehouse
  3. For event tracking
  4. For explaining why a record exists in a fact table

Answer : B




 
61) An Order number in a Sales fact table is an Example of

  1. Junk Dimension
  2. Transaction Identifier
  3. Degenerative Dimension
  4. None of the above

Answer : C

 
 
62) ___________ provide structured labeling information to otherwise unordered numeric measures

  1. Facts
  2. Dimensions
  3. Tables
  4. Joins

Answer : B

 
 
63) Can a single dimension definition contain multiple hierarchies

  1. No
  2. Yes
  3. No, separate dimesnions are required
  4. None of the above

Answer : B

 
 
64) Dimension where data quality cannot be guaranteed

  1. Conformed dimension
  2. Dirty Dimension
  3. Big Dimension
  4. None of the above

Answer : B

 
 
65) Invoice number in Sales Fact is

  1. a conformed dimension
  2. a degenerate dimension
  3. a slowly changiing dimension
  4. None of the above

Answer : B

 
 
66) What is a ‘Degenerate’ dimension?

  1. A dimensional attribute embedded in a fact table
  2. A fact table that stores only events
  3. A dimension table that contains embedded fact information
  4. None of the above

Answer : A

 
 
67) what is a junk dimension?

  1. It is a table placed between a fact and a dimension table or between two dimension tables so that the dimensions can become multivalued.
  2. A degenerate dimension (DD) acts as a dimension key in the fact table, however does not join to a corresponding dimension table because all its interesting attributes have already been placed in other analytic dimensions.
  3. It is a dimension is a convenient grouping of flags and indicators.
  4. It is a dimension that means the same thing with every possible fact table to which it can be joined

Answer : C

 
 
68) Role Playing is

  1. A dimension relating to moe than one fact table
  2. A dimension relating to different fact tables with different meanings
  3. A dimension relating to same fact table in different roles
  4. All of the above

Answer : C

 
 
69) Any analysis which  involves on “what I want to know” is a

  1. Dimension
  2. Attribute
  3. Flag
  4. Fact

Answer : D

 
 
70) Average marks’ is not an example of

  1. Additive Fact
  2. Semi-Additive Fact

Answer : A




 
71) Fact less Fact tables are used

  1. For event tracking
  2. For handling multi valued dimensions
  3. As a helper table
  4. None of the above

Answer : A

 
 
72) What is the characteristic of Conformed Fact?

  1. Fact occurs in more than one location
  2. Underlying fact definition is common
  3. Transformation rule to compute factual measure in identical
  4. All of these

Answer : D

 
 
73) Which is NOT true related to Factless fact table ?

  1. Captures many-to-many relationships between dimension tables
  2. Can have factual measures
  3. Can have only information about Keys
  4. None of the above

Answer : B

 
 
74) Which of following describes a Factless fact table?

  1. Tracks events
  2. Captures many-to-many relationships between dimension tables
  3. Stores no factual measures
  4. All of the above

Answer : D

 
 
75) Which of the following is an example of factless fact?

  1. Class attendance
  2. Sales
  3. Purchase
  4. Inventory levels

Answer : A

 
 
76) Which of the following is not characteristic of Conformed facts

  1. Same names across all fact tables
  2. Same definitios across all fact tables
  3. Same formulas used for calculation
  4. None of the above

Answer : A

 
 
77) What is NOT a characteristic of Snow flake model?

  1. Low cardinality attributes are moved to new sub-dimension tables
  2. Further normalization of the Star schema model
  3. Lowers redundancy
  4. Flat dimension table

Answer : D

 
 
78) Which is the most normalized structure within a star schema?

  1. Fact Table
  2. Dimension Table
  3. Both A & B
  4. None

Answer : A

 
 
79) _____ table have the characteristic of “Slicing and Dicing”

  1. Fact Table
  2. Dimension Table
  3. Look-Up Table
  4. All of the above.

Answer : B

 
 
80) Data granularity depends on

  1. Source system data
  2. Dimension keys linked to a fact
  3. Can not be determined from star schema
  4. Hierarchy of Dimension table

Answer : B




 
81) Dimensionality refers to

  1. The level of detail of data that is held in the fact table
  2. The data that describes the transactions in the fact table.
  3. The level of detail that is held in the Data Warehouse
  4. The number of dimension tables that exist in a star schema

Answer : B

 
 
82) Drill across means drill both Fact and Dimension table”

  1. No; Only Dim tables
  2. yes

Answer : A

 
 
83) Factless fact table has only information about

  1. Data
  2. Measure
  3. Keys
  4. All of the above

Answer : C

 
 
84) For multi dimensional design which of the following statements are generally true

  1. It is highly normalized
  2. It is highly denormalized
  3. Is based on an ER Model
  4. Is based on the users requirements

Answer : B

 
 
85) Hierarchy in a dimension is not created to show

  1. Levels
  2. History
  3. Parents
  4. Parallel relationship

Answer : B

 
 
86) Identify Type of Fact?

  1. View
  2. Table
  3. Additive
  4. Materialized View

Answer : C

 
 
87) If you are going for New Fact Tables for Aggregates, What are the Strategies that can be followed

  1. COLLAPSED DIMENSION AGGREGATES
  2. LOST DIMENSION AGGREGATES
  3. SHRUNKEN DIMENSION AGGREGATES
  4. All the above

Answer : D

 
 
88) In which of these scenarios snowflake schema is not  used?

  1. Dimension is very sparse
  2. Improve the performance of query in the data warehouse
  3. Storing the information in same dimension to result in duplication of records
  4. reducing the redundancy in the dimension table

Answer : B

 
 
89) The process of viewing detailed data from summarized data is known as

  1. Drill up
  2. Drill down
  3. Drill through
  4. Drill across

Answer : B

 
 
90) What does level of Granularity of a fact table signify?

  1. Granularity is a level of representation of measures and metrics
  2. Granularity means just data
  3. Granularity indicates facts

Answer : A




 
91) What is Drill down (roll down)?

  1. reverse of roll-up
  2. from higher level summary to lower level summary or detailed data, or introducing new dimensions
  3. Both A and B
  4. None of the above

Answer : C

 
 
92) What is meant by fact table ‘grain’?

  1. The most detailed level of transaction captured in the fact table
  2. The most ‘granular’ data that is captured in the datawarehouse
  3. ‘Grains’ of transaction in the source system
  4. None of the above

Answer : A

 
 
93) What is NOT the characteristic of Dimension table?

  1. Descriptions of the business
  2. Constant
  3. Maintains facts about the business
  4. Enables slicing and dicing by different variables

Answer : C

 
 
94) What is the primary objective of dimensional modeling?

  1. remove redundancy in the data
  2. optimizing
  3. query performance
  4. facilitate retrieval of individual transaction
  5. None of the above

Answer : B

 
 
95) What is true of the multidimensional model?

  1. It typically requires less disk storage
  2. It typically requires more disk storage
  3. Typical business queries requiring aggregate functions take more time
  4. Increasing the size of a dimension is difficult

Answer : B

 
 
96) Where will you find measures in a data warehouse?

  1. Dimension tables
  2. Fact tables
  3. Helper tables
  4. Look up tables

Answer : B

 
 
97) Which Dimension can attach to multiple facts ?

  1. Pseudo Dimension
  2. Junk Dimension
  3. Derived Dimension Table
  4. Confirmed Dimension

Answer : D

 
 
98) Which is NOT an example of Fact Table?

  1. Revenue
  2. Time
  3. Gross Margin
  4. Cost

Answer : B

 
 
99) Which of the below is true for Role Playing Dimensions?

  1. Same grain
  2. Views of the same table
  3. Different roles in a fact
  4. All of the above

Answer : D

 
 
100) Which of the following are examples of dimensions?

  1. Location
  2. Product
  3. Time
  4. All of the above

Answer : D




 
101) Which of the following is not true about dimensional modeling?

  1. Optimized for retrieval
  2. Maximize understandability
  3. Remove the redundancy of data
  4. helps historical tracking of information

Answer : C

 
 
102) Which of the following statements is true?

  1. The fact table of a data warehouse is the main store of descriptions of the transactions stored in a DWH
  2. The fact table of a data warehouse is the main store of all of the recorded transactions over time.
  3. A fact table describes the granularity of data held in a DWH
  4. A fact table describes the transactions stored in a DWH

Answer : B

 
 
103) Which of the following holds data only triggered by Business Operations Event

  1. Periodic Snapshot Fact
  2. Transactional Fact
  3. Factless Fact
  4. Dimensions

Answer : B

 
 
104) Code Tables are examples of

  1. Dependency
  2. Domain
  3. Association
  4. Transaction

Answer : B

 
 
105) Which one of the following is an example of additive measure?

  1. price of a product
  2. quantity sold
  3. inventory
  4. level
  5. account balances

Answer : B

 
 
106) Which one of the following is an example of non-additive measure?

  1. price of a product
  2. inventory
  3. level
  4. quantity sold
  5. Room temprature

Answer : D

 
 
107) Which one of the following is an example of semi-additive measure?

  1. account balances
  2. quantity sold
  3. dollars sold
  4. None of the above

Answer : A

 
 
108) A Percentage measure attribute is

  1. Better Stored in the fact table
  2. Better calculated in views
  3. Better to be derived by the OLAP tool
  4. Better to be calculated by End User Groups

Answer : C

 
 
109) Having an order number as part of a fact table is

  1. Degenerative Fact
  2. Factless Fact
  3. Aggregate fact
  4. None of the above

Answer : A

 
 
110) Which of the following table, data gets loaded once and gets updated on various Business stages.

  1. Periodic Snapshot Fact
  2. Transactional Fact
  3. Accumulating Snapshot Fact
  4. Factless Fact

Answer : C




 
111) What is NOT captured as Attribute metadata?

  1. Description
  2. Data type
  3. Domain
  4. Frequency of change

Answer : D

 
 
112) What is NOT captured as part of business metadata?

  1. Name
  2. Business definition
  3. Sample values
  4. Created / last updated details

Answer : D

 
 
113) What is NOT captured as part of metadata for entity type?

  1. Definition
  2. Examples
  3. Sparsity
  4. Expected volumes

Answer : C

 
 
114) Why is denormalization beneficial to read operations?

  1. Indexes are sorted.
  2. Disk I/O is reduced.
  3. Complex joins are avoided
  4. Easy Data flow

Answer : C

 
 
115) All of the following are common denormalization techniques EXCEPT which one?

  1. Duplicating non-key columns across tables
  2. Adding derived or summary columns to tables
  3. Adding non-key columns to indexes
  4. Creating fake arrays in tables using multiple columns

Answer : C

 
 
116) Grouping of Individuals, Organization under a single entity named Party is an example of:

  1. Normalization
  2. Denormalization
  3. Generalization
  4. Specialization

Answer : C

 
 
117) A situation where single dimension appears several times in the same fact table

  1. Dirty dimension
  2. Conformed Dimension
  3. Role playing dimension
  4. None of the above

Answer : C

 
 
118) Which of the following is the name of a table containing  certain groups of attributes from a dimension table

  1. conformed dimension
  2. helper tables
  3. junk dimension
  4. mini dimension

Answer : D

 
 
119) If we decide to place a dimensional attribute as a non-key attribute in a Fact table, what is it called?

  1. Semi-additive Measure
  2. Degenerate Dimension
  3. Fully Additive measure
  4. None of the choices

Answer : B

 
 
120) What type of measure will ‘Units on Hand’ be classified if it is associated with Time dimension?

  1. Semi-additive Measure
  2. Fully Additive Measure
  3. Non-additive Measure
  4. None of the choices

Answer : A




 
121) Identify the statement that is NOT TRUE related to Fact tables

  1. Fact tables always have a multipart key, in which each component of the key joins to a single dimension table
  2. Fact tables contain the numeric, additive fields that are best thought of as the measurements of the business
  3. Fact tables can consist of nothing but keys
  4. is a data element that categorizes each item in a data set into non-overlapping regions.

Answer : D

 
 
122) If we need to capture only the latest state of a Transaction(e.g. Order Fulfillment) in a Fact table at any time and if we update the Fact table rows to achieve this, what type of Fact table is this?

  1. Periodic Snapshot
  2. Transaction Grain Fact
  3. Factless Fact
  4. Accumulating Snapshot

Answer : D

 
 
123) Domain Pattern is implemented in

  1. Code tables
  2. Look up tables
  3. Validation lists
  4. All of these

Answer : D

 
 
124) What kind of relationship is possible between Super type – Sub type entities?

  1. one to many
  2. many to one
  3. one to one optional
  4. one to one mandatory

Answer : C

 
 
125) When can Super type – Sub type data pattern be used?

  1. Header / details data structure
  2. Snowflaking
  3. A grouping of entities share common characteristics and also has some unique ones
  4. None of these

Answer : C

 
 
126) which technique helps to determine that to which subtype one instance of a supertype belongs.

  1. Primary Key for an entity
  2. Foreign key
  3. Subtype discriminator
  4. None of these

Answer : C



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