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Questions 1–10 of 10

1. What is Knowledge Graph Modelling?
Question 1A. The practice of representing information as a network of interconnected entities and relationships enriched with semantic meaningB. The practice of storing data as isolated records in tablesC. The practice of classifying terms into parent-child hierarchiesD. The practice of drawing charts for dashboardsE. The practice of normalising data to third normal form
2. In a knowledge graph, what do nodes and edges represent?
Question 2A. Nodes are tables and edges are columnsB. Nodes are servers and edges are network linksC. Nodes are entities such as Customer or Loan, and edges are relationships such as applies for or ownsD. Nodes are processes and edges are decisionsE. Nodes are attributes and edges are data types
3. How do relationships in a knowledge graph differ from those in a traditional relational model?
Question 3A. Knowledge graphs have no relationshipsB. Relational models make relationships more visible than graphsC. Knowledge graphs store relationships only as textD. In a knowledge graph relationships are first-class elements that are visible and traversable, rather than embedded in foreign keysE. There is no difference
4. Which everyday example is used to explain why connections matter in a knowledge graph?
Question 4A. A school libraryB. A book indexC. A spreadsheet of employeesD. A social network where finding mutual friends means following connections between peopleE. A homework submission process
5. Which analytical question is given as an example that graph traversal can answer?
Question 5A. The average salary of employeesB. The number of columns in the loan tableC. The order of steps in a loan processD. The Greek origin of the word taxonomyE. Detecting fraud networks by following connections between guarantors, borrowers and shared addresses
6. How do ontologies relate to knowledge graphs?
Question 6A. Ontologies replace knowledge graphsB. Ontologies define vocabulary and rules, and the knowledge graph operationalises them into a living network of connected dataC. Knowledge graphs define the vocabulary for ontologiesD. They are unrelated techniquesE. Ontologies store the data and knowledge graphs store the rules
7. Why is Knowledge Graph Modelling often described as schema-light?
Question 7A. Because it has no structure at allB. Because new nodes and relationships can be added without redesigning rigid table structuresC. Because it stores less data than a relational modelD. Because it does not use ontologiesE. Because it only works with small datasets
8. Which of the following is NOT named as a common use of knowledge graphs?
Question 8A. Search enginesB. Recommendation systemsC. Fraud detectionD. Enterprise data fabricsE. Payroll processing
9. In the loan example of a knowledge graph, which relationship connects a Loan Officer to a Loan Application?
Question 9A. applies forB. has approval decisionC. reviewsD. is linked to repayment scheduleE. guarantees
10. In the comparison of FCO-IM, Taxonomy, Ontology and Knowledge Graph, what does each contribute?
Question 10A. FCO-IM = precise facts; Taxonomy = classification subset; Ontology = shared vocabulary and logic; Knowledge Graph = dynamic, connected intelligenceB. FCO-IM = classification; Taxonomy = precise facts; Ontology = connected intelligence; Knowledge Graph = vocabularyC. All four provide the same connected intelligenceD. FCO-IM = shared vocabulary; Ontology = precise facts; Taxonomy = connected intelligence; Knowledge Graph = classificationE. Taxonomy = full reasoning; Ontology = classification only