Data Modellingwith Graph Theory — Learn free

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Questions 1–10 of 10

1. When is data modelling with graph theory particularly useful?
Question 1A. When relationships between data points are as important as the data itselfB. When the data consists of a single flat tableC. When there are no relationships between recordsD. When data only needs to be archivedE. When the data is purely numeric
2. In data modelling with graphs, how are data entities and their connections represented?
Question 2A. Entities as columns and connections as rowsB. Entities as nodes and connections as edgesC. Entities as edges and connections as nodesD. Entities as files and connections as foldersE. Entities as keys and connections as indexes
3. Which graph-based data model uses triples of subject, predicate and object, and is common in the semantic web and knowledge graphs?
Question 3A. Property Graph ModelB. HypergraphC. RDF Graph ModelD. Relational ModelE. Star Schema
4. What is distinctive about a hypergraph?
Question 4A. Its nodes have no propertiesB. It stores data only as triplesC. It cannot represent group membershipsD. Its edges can connect more than two nodesE. It is used only by Neo4j
5. In the Property Graph Model used by graph databases such as Neo4j, what do nodes and edges carry?
Question 5A. Nothing; they are plain identifiersB. Only a numeric weightC. Only a text labelD. A single foreign key eachE. Properties as key-value pairs, such as Name: Alice, Age: 25 on a node and Since: 2020 on an edge
6. Which of the following is NOT one of the steps for modelling data with graph theory?
Question 6A. Normalise all data into third normal formB. Identify entities and relationshipsC. Define node and edge typesD. Determine attributes and propertiesE. Optimise for queries
7. In the e-commerce graph model example, which of the following is an edge rather than a node?
Question 7A. CustomerB. BuysC. ProductD. CategoryE. Laptop
8. What does 'Optimise for Queries' recommend when modelling a graph for an e-commerce platform?
Question 8A. Removing all edges to simplify the graphB. Storing every product in a single nodeC. Adding direct edges for frequent queries, such as 'Customers who bought X also bought Y'D. Converting the graph into a spreadsheetE. Using only undirected edges
9. Which application of graph data modelling involves analysing transaction networks to detect unusual patterns or cycles?
Question 9A. Social networksB. Knowledge graphsC. Supply chain and logisticsD. Fraud detectionE. Recommendation systems
10. Which of the following is listed as a challenge of graph-based data modelling?
Question 10A. Schema inflexibilityB. Slow traversal of connected dataC. Inability to represent relationshipsD. Lack of real-time insightsE. The learning curve of graph query languages such as Cypher, Gremlin and SPARQL