GraphRAG Knowledge Graphs Quiz

Answers and explanations appear as you take the quiz.

1. What core structure does GraphRAG build that distinguishes it from standard RAG?
   • A relational SQL schema of users
   • A neural network of pixels
   • A knowledge graph of entities and their relationships
   • A list of stopwords
2. What query scope motivated community reports in Microsoft's 2024 GraphRAG work?
   • Only changing a sentence's spelling
   • Collection-wide themes and summaries whose evidence spans many documents
   • Only measuring a video file's frame rate
   • A guarantee that all narrow lookups must use graph traversal
3. What algorithm does GraphRAG commonly use to cluster related entities into communities?
   • Bubble sort
   • Gradient descent
   • RSA encryption
   • Leiden community detection
4. What does GraphRAG's model-based extraction stage produce from source chunks?
   • Structured entity and relationship records with descriptions
   • Only an executable operating-system image
   • Only random names unrelated to the source
   • Only audio waveforms with no text records
5. How do documented GraphRAG local and global query strategies use context differently?
   • Local means Wi-Fi and global means satellite connectivity
   • Local search uses entity-related and source context; global search aggregates community reports
   • Local works only with images while global excludes all documents
   • Both strategies discard extracted information before generation
6. Which organization popularized the GraphRAG approach in 2024?
   • A 1970s telephone company
   • A fast-food chain
   • Microsoft Research
   • An airline alliance
7. Why should an important GraphRAG relationship be checked against its source documents?
   • The extraction model can make errors, and a graph edge does not independently establish the relationship's truth
   • Every extracted edge is automatically a verified fact
   • Graph storage replaces the need for source evidence
   • Community detection proves the original documents cannot be wrong
8. What additional upfront work can GraphRAG indexing require compared with simply preparing text chunks?
   • No model calls or derived records of any kind
   • Automatic retraining of every user's language model
   • Publishing every source document to social media
   • Model-based entity and relationship extraction and generation of community reports