Practice English vocabulary for graph databases: nodes, edges, properties, labels, graph query languages like Cypher and Gremlin, and traversal concepts.
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In a property graph model, what is a 'node'?
A node (also called a vertex) is the fundamental unit representing an entity in a property graph. Nodes can have labels (types) and key-value properties attached to them.
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Which statement best describes an 'edge' (relationship) in a directed graph?
In a directed graph, an edge has a start node and an end node, giving it a direction. In Neo4j, every relationship has a type (e.g., KNOWS, WORKS_AT) and can carry properties.
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What does 'graph traversal' mean in practice?
Graph traversal means starting at a node and following edges to visit neighbouring nodes. It is the core operation in graph queries — used in path finding, recommendation engines, and fraud detection.
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What is Cypher used for?
Cypher is Neo4j's declarative graph query language. Its ASCII-art syntax makes it intuitive: (alice)-[:KNOWS]->(bob) reads naturally as 'Alice knows Bob'. It supports MATCH, CREATE, MERGE, and RETURN clauses.
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How does Gremlin differ from Cypher?
Gremlin (Apache TinkerPop) is an imperative, step-based traversal language: g.V().has('name','Alice').out('KNOWS').values('name'). It is database-agnostic and works with JanusGraph, Amazon Neptune, and others alongside Neo4j.
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Code Review Comment: 'I'm seeing a lot of 'node_id' properties being created without any clear parent node. Are we sure this isn't just a temporary placeholder for linking these new users to existing accounts? It seems like a potential performance bottleneck later on.' Which of the following best explains the reviewer's concern regarding the node_id property in this graph database context?
The reviewer's comment highlights a critical aspect of graph database design: referential integrity. Creating node_id properties without established parent nodes can lead to orphaned records and difficulties in querying related data efficiently – a common performance bottleneck. The core issue isn't just about an 'error,' but the potential for future problems with data relationships.
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Slack Message: 'Hey @john_doe, I'm trying to query all customers who placed an order in the last week using Gremlin. The initial results are returning a lot of false positives – it seems like the traversal isn't filtering correctly. Any ideas?' What does John likely need to adjust to improve the accuracy of his Gremlin query?
The problem lies with the traversal path itself. Gremlin's flexibility can lead to unintended results if not carefully constructed. John needs a more precise traversal – adding filters or constraints within the query will limit the nodes considered during the traversal, reducing false positives and ensuring he only gets orders from the last week. Cypher isn't necessarily the solution; it's about controlling *how* Gremlin explores the graph.
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PR Description: 'This commit adds a new Cypher query to retrieve all products linked to a specific customer. The query utilizes 'traverse' to follow the 'purchased' edge from the customer node to its related product nodes.' What does the term 'traverse' refer to in this context?
In graph databases, 'traverse' specifically describes the process of navigating through relationships. The 'traverse' function in Cypher (and Gremlin) allows you to follow edges – represented by the 'purchased' edge in this example – from one node to another, building a path based on defined relationships within the database. It's about exploring connections, not data movement or creating new nodes.
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API Response (JSON): {
"status": "success",
"query_result": [
{
"product_id": "P123",
"product_name": "Laptop Pro",
"price": 1200
},
{
"product_id": "P456",
"product_name": "Mouse Wireless",
"price": 25
}
]
} What does this API response indicate about the graph database's underlying structure?
This JSON response represents the result of a query against a graph database. The structure – with nodes (products) and edges implicitly defined by the relationships in the data – points to a property graph model. Each product is identified by its `product_id`, and the relationship between customers and products is implied through the inclusion of both in the response, indicating connected nodes linked by edges.
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Standup Update: 'I'm currently working on optimizing our queries to reduce latency. We're experimenting with different traversal strategies and considering migrating some of the more complex queries to Cypher.' What is a primary reason for exploring Cypher in this scenario?
Cypher's strength lies in its declarative syntax – designed to clearly express relationship traversals. This is particularly beneficial when dealing with complex queries involving multiple layers of connected data. While Cypher has performance advantages in certain cases (particularly for structured relationships), the core reason for exploring it here is to leverage its readability and ease of use for defining and executing these intricate traversal paths.
What will I practise in "Graph Database Vocabulary"?
Practice English vocabulary for graph databases: nodes, edges, properties, labels, graph query languages like Cypher and Gremlin, and traversal concepts.
How many exercises are in this module?
This module has 10 multiple-choice exercises, each with instant feedback and a full explanation of the correct answer.
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You'll immediately see which answer was correct, plus a full explanation covering the vocabulary and reasoning behind it — mistakes are where most of the learning happens.
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Where can I find more Knowledge Graph Vocabulary exercises?
Browse the full Knowledge Graph Vocabulary hub for related drills, or check the "Next up" link below to continue with a connected topic.
How is this different from reading an article on the same topic?
Articles explain vocabulary and concepts in prose; this exercise tests and reinforces that vocabulary through active recall with immediate feedback — the two work best together.
Who writes these exercises?
Every exercise is written by the CoderSlingo team, drawing on real workplace English used in IT roles, then reviewed for accuracy and clarity.