Semantic Data Modelling — Learn free

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Questions 1–10 of 10

1. What is Semantic Data Modelling?
Question 1A. The practice of designing data structures enriched with explicit business meaning so humans and machines interpret data consistentlyB. The practice of defining a standard exchange format between systemsC. The practice of arranging concepts in parent-child hierarchiesD. The practice of indexing database tablesE. The practice of drawing swimlane diagrams
2. What is the primary goal of Semantic Data Modelling?
Question 2A. To reduce the number of integrationsB. To speed up transaction processingC. To create data models that are self-descriptive and semantically preciseD. To eliminate the need for ontologiesE. To store data as flat files
3. In a semantic model, how is a field such as CustomerID treated differently from a traditional model?
Question 3A. It is stored as text instead of a numberB. It is removed from the modelC. It is encryptedD. It is linked to a formal definition stating it identifies a person or organisation holding an account in a business contextE. It is renamed to a shorter code
4. What are the subject-predicate-object patterns used in semantic modelling called?
Question 4A. TuplesB. TablesC. TreesD. TriplesE. Tokens
5. Which of the following is an example of a semantic triple in the loan domain?
Question 5A. Customer123 appliesFor LoanApplication456B. SELECT * FROM loansC. Loan Products → Personal LoansD. Customer table has a foreign key to Loan tableE. The loan officer works in swimlane two
6. How can a semantic model infer that a loan is active?
Question 6A. By reading a manually entered status flagB. By counting repaymentsC. By checking the loan officer's notesD. By sorting loans by dateE. Because the ontology defines that an approved loan becomes an active loan, so an Approved decision lets the system infer the loan is active
7. How does Semantic Data Modelling differ from Canonical Data Modelling?
Question 7A. Canonical models standardise meaning; semantic models standardise structureB. They are identicalC. Semantic models only work for unstructured dataD. Canonical models enable inference; semantic models do notE. Canonical models standardise structure for exchange; semantic models go further by standardising meaning so machines can reason
8. In the analogy of sentences, what does an Ontology represent compared with a Knowledge Graph?
Question 8A. The ontology is the network of sentences; the knowledge graph is the dictionaryB. The ontology is the dictionary and grammar; the knowledge graph is the network of sentences linked for queriesC. Both are sentences with full contextD. The ontology is the sentence written with meaningE. Neither is related to sentences
9. In Core Data Modelling, which techniques typically follow the first journey, moving directly from conceptual representation to physical design with little or no logical stage?
Question 9A. Data Vault, Anchor Modelling and dimensional modellingB. Hierarchical models, network models, event-driven architectures, JSON or XML schema design and API modellingC. Temporal modelling and Unified Star SchemaD. Object-oriented modelling and NoSQL designE. Focal Point Modelling and Hook structures
10. What is the key insight about the two architectural journeys in Core Data Modelling?
Question 10A. Every technique requires a separate logical layerB. No technique ever reaches physical implementationC. Not all techniques need a separate logical layer, but all must ultimately move from concept to implementationD. Only NoSQL techniques reach implementationE. The logical layer replaces the physical layer