| Date | November 2019 | Marks available | 2 | Reference code | 19N.2.SL.TZ0.2 |
| Level | SL | Paper | 2 | Time zone | no time zone |
| Command term | Construct | Question number | 2 | Adapted from | N/A |
Question
Taxonomy is the part of science that focuses on naming and classifying or grouping organisms. Swedish naturalist, Carl Linnaeus, developed the current hierarchical classification system of taxonomic classification in the 1700s. This classification is hierarchical so one class has many orders, and one order has many families.
Birds can be classified under the Linnaean hierarchical taxonomy as “Aves”. In one system of classification, Aves has 23 orders, one of which is the order Ciconiiformes, which holds six families within it.
Spoonbills constitute one of the families within the order Ciconiiformes. Data can be collected about spoonbills, such as length of the bill or beak, and is included in the database.
Construct an entity-relationship-diagram that shows the relationships between the class, order and family.
Outline, using an example, how data validation can ensure that data is entered into the birds database correctly.
Outline, using an example, how data verification can ensure that data is entered into the birds database correctly.
Distinguish between data and information when entering the length of a spoonbill’s beak in the database.
Identify two characteristics of a conceptual schema.
Explain why the use of data modelling is critical to the success of a database, such as the one used in this scenario.
Explain, using the example described in the scenario, why referential integrity is important in databases.
Markscheme
Award [2 max].
Award [1] for each correct relationship with labels.
Award [1] for a top-down diagram showing the relationships.
One CLASS contains many ORDER;
One ORDER contains many FAMILY;
Award [2 max].
Note: original MS did not make any sense
Length check;
Could ensure only a value within an acceptable range is entered (allow example);
Type/format check on bill;
Only numerical (int/float etc.);
Award [2 max].
Double entry;
Will only allow data entry if both match;
Proof reading;
Will give a visual check that the data is correct;
Award [2 max].
When the length of the bill / beak of the spoonbill is measured it is simply a decontextualized figure such as 10 cm;
When it is included in the database and interrogated it will have a meaning, so it then becomes information;
The database will contain only the figure “10”;
the “cm” will not be included and is part of the meaning of the attribute/field.
Note: the “information” mark must reference the database in some way
Award [2 max].
A high-level / overall / general view of the database;
Shows the main concepts;
Shows the main relationships,
Can be described by a Data Structure Diagram;
Award [3 max].
Data modelling gives a visual representation of the proposed system;
So that all stakeholders have the same understanding of the system;
Shows basic relationships / establishes naming conventions etc.;
Some that developers are able to develop the actual database;
Avoiding issues such as redundancy/lack of integrity/lack of consistency;
The data model will take the information from the conceptual schema and logical schema to informs the physical model for the development of the database;
A lack of modelling may result in a structurally deficient implementation with poor data integrity / redundant data;
This may mean developing prototypes of the database to test whether the proposed structure of the database will function as intended;
Award [3 max].
Note: only award full marks if there is a valid reference to the scenario.
Referential integrity requires that a value used as a foreign key corresponds to a value of a primary key / validates the database through relationships;
Therefore if the Ciconiiformes/order of the Spoonbill is not in the database, you will not be able to add it because of referential integrity;
Referential integrity constraints;
Ensure that if a record is updated or deleted, these changes will made in related cells and the possibility of update anomalies is reduced / eliminated;
Examiners report
Most of the candidates clearly identified the main entities but failed to establish and correctly label the relationships.
Some candidates showed a good understanding of data validation and data verification with appropriate use of examples related to the scenario. However, many candidates provided answers that were generic, which showed confusion with both concepts.
Some candidates showed a good understanding of data validation and data verification with appropriate use of examples related to the scenario. However, many candidates provided answers that were generic, which showed confusion with both concepts.
Most candidates had no problem identifying what data was, but were not able to clearly distinguish the concept of information within the scenario.
A few candidates were able to correctly identify the characteristics of a conceptual schema.
This was not well answered. Many candidates provided vague responses and just a few referred to data modeling as being critical process for avoiding issues such as redundancy, lack of integrity or lack of consistency.
Most of candidates could not explain referential integrity with respect to the scenario.
