100+ Free Neo4j GDS Certification Practice Questions

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What is the key difference between a native projection and a Cypher projection in GDS?

Native projections use pre-existing graph schema for fast loading; Cypher projections use custom Cypher queries for flexibility

Native projections require schema indexes; Cypher projections work on any graph

Native projections only support directed graphs; Cypher projections support both

Native projections store data on disk; Cypher projections keep data in memory

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Neo4j Certified Professional

Practice Questions100 questions

2026 Statistics

Key Facts: Neo4j GDS Certification Exam

~50

Exam Questions

Neo4j GraphAcademy

80%

Passing Score

Neo4j

60 min

Exam Duration

Neo4j

Free

Exam Fee

Neo4j GraphAcademy

2 years

Credential Validity

Neo4j

Online

Delivery

graphacademy.neo4j.com

Neo4j Graph Data Science Certification is a free online exam with approximately 50 questions in 60 minutes requiring 80% to pass. Delivered through graphacademy.neo4j.com. Covers GDS projections, algorithm execution modes (stream/stats/mutate/write), centrality, community detection, pathfinding, similarity, embeddings, and ML pipelines. Valid for 2 years.

Sample Neo4j GDS Certification Practice Questions

Try these sample questions to test your Neo4j GDS Certification exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which GDS procedure creates a native projection of nodes labeled 'Person' and 'KNOWS' relationships?

A.CALL gds.graph.project('myGraph', 'Person', 'KNOWS')

B.CALL gds.graph.create('myGraph', 'Person', 'KNOWS')

C.CALL gds.project.graph('myGraph', 'Person', 'KNOWS')

D.CALL gds.native.project('myGraph', 'Person', 'KNOWS')

Explanation: gds.graph.project is the current GDS procedure for creating named in-memory graphs (native projection). gds.graph.create was the older deprecated API.

2What is the key difference between a native projection and a Cypher projection in GDS?

A.Native projections use pre-existing graph schema for fast loading; Cypher projections use custom Cypher queries for flexibility

B.Native projections require schema indexes; Cypher projections work on any graph

C.Native projections only support directed graphs; Cypher projections support both

D.Native projections store data on disk; Cypher projections keep data in memory

Explanation: Native projections leverage existing labels/relationship types and are significantly faster for large graphs. Cypher projections allow arbitrary filtering and transformation but are slower.

3Which GDS execution mode returns results row-by-row and does NOT write back to Neo4j?

A.stream

B.stats

C.mutate

D.write

Explanation: stream mode yields results row by row without persisting anything to the database, ideal for exploration or piping into downstream operations.

4After running `CALL gds.pageRank.mutate('myGraph', {mutateProperty: 'score'})`, where is the 'score' property stored?

A.In the in-memory projected graph only

B.Written to Neo4j node properties immediately

C.Written to a GDS catalog result set

D.Stored in a temporary on-disk file

Explanation: mutate mode stores computed properties in the in-memory GDS graph projection. You must subsequently call a write procedure to persist them to the Neo4j database.

5Which Cypher correctly streams PageRank scores for all nodes in a named graph?

A.CALL gds.pageRank.stream('myGraph') YIELD nodeId, score RETURN gds.util.asNode(nodeId).name AS name, score ORDER BY score DESC

B.CALL gds.pageRank.run('myGraph') YIELD nodeId, score RETURN nodeId, score

C.CALL gds.algorithms.pageRank('myGraph') YIELD nodeId, score RETURN nodeId, score

D.MATCH (n) CALL gds.pageRank.stream(n) YIELD score RETURN n.name, score

Explanation: gds.pageRank.stream with YIELD nodeId, score is the correct streaming API. gds.util.asNode converts the internal nodeId back to a Neo4j node for property access.

6PageRank uses a damping factor. What is the default damping factor in GDS and what does it represent?

A.0.85; probability that a random surfer continues following links rather than jumping to a random node

B.0.15; probability of reaching a page from an external link

C.1.0; ensures all PageRank mass is conserved in the graph

D.0.5; balances incoming and outgoing link contributions equally

Explanation: The default damping factor is 0.85. It represents the probability a random surfer follows a link versus teleporting to a random node, preventing rank sinks.

7How does ArticleRank differ from PageRank in GDS?

A.ArticleRank reduces the influence of high-degree nodes by weighting contributions by the inverse of the source node's degree

B.ArticleRank only considers incoming relationships, while PageRank considers both

C.ArticleRank uses a higher default damping factor than PageRank

D.ArticleRank is an undirected algorithm; PageRank requires directed graphs

Explanation: ArticleRank penalizes contributions from highly connected (high-degree) nodes, giving less weight to links from hubs, which addresses the hub inflation problem in PageRank.

8Betweenness Centrality in GDS measures which property of a node?

A.The fraction of all shortest paths in the graph that pass through the node

B.The number of direct neighbors of the node

C.The average distance from the node to all other nodes

D.The probability that a random walk visits the node

Explanation: Betweenness Centrality counts how many shortest paths between pairs of other nodes pass through a given node, identifying bridges and brokers in the network.

9Which GDS procedure computes Degree Centrality and what does it return by default?

A.gds.degree.stream returns nodeId and score where score equals the sum of relationship weights for each node

B.gds.degreeCentrality.stream returns nodeId and degreeCount

C.gds.centrality.degree.stream returns nodeId and in-degree

D.gds.graph.degree.stream returns nodeId and normalized degree

Explanation: gds.degree.stream is the correct procedure. By default it returns nodeId and score, where score is the count (or weighted sum) of relationships. The orientation setting controls whether in-, out-, or all-degree is measured.

10Which GDS community detection algorithm iteratively moves nodes to the community of their most frequent neighbor and is known for fast runtime?

A.Label Propagation

B.Louvain

C.WCC

D.K-Means

Explanation: Label Propagation assigns each node the label held by the majority of its neighbors, iterating until stability. It is near-linear in time complexity, making it very fast.

About the Neo4j GDS Certification Exam

The Neo4j Graph Data Science Certification validates skills in graph analytics using the GDS library. It covers native and Cypher projections, graph catalog management, centrality algorithms (PageRank, Betweenness, Closeness, Degree), community detection (Louvain, Label Propagation, WCC), pathfinding (Dijkstra, A*, Yen's), similarity algorithms (Node Similarity, KNN), graph embeddings (FastRP, Node2Vec, GraphSAGE), and ML pipelines for node classification and link prediction.

Questions

50 scored questions

Time Limit

60 minutes

Passing Score

80%

Exam Fee

Free (Neo4j / GraphAcademy)

Neo4j GDS Certification Exam Content Outline

~20%

GDS Library and Graph Projections

GDS library installation and version, native graph projection with gds.graph.project, Cypher projection for custom graphs, graph catalog operations (gds.graph.list, gds.graph.drop), node label and relationship type filtering.

~20%

Centrality Algorithms

PageRank (damping factor, iterations, convergence), Betweenness Centrality (shortest paths traversal), Closeness Centrality (inverse of avg distance), Degree Centrality, Article Rank, and execution via stream/stats/mutate/write modes.

~20%

Community Detection

Louvain Modularity (hierarchical, modularity score), Label Propagation (seed labels, weighted), Weakly Connected Components (component IDs), Strongly Connected Components (directed graphs), Triangle Count and Local Clustering Coefficient.

~15%

Pathfinding Algorithms

Dijkstra source-target and SSSP (single source shortest path), A* algorithm with heuristic, Yen's k-shortest paths, Breadth First Search, Depth First Search, and Random Walk.

~10%

Similarity Algorithms

Node Similarity with Jaccard and Overlap similarity functions, K-Nearest Neighbors (KNN) with node property vectors, topK/topN configuration, and building similarity graphs.

~15%

Embeddings and ML Pipelines

FastRP (fast random projection, embeddingDimension, iterations), Node2Vec (walkLength, walks per node, p/q parameters), GraphSAGE (aggregator types), node classification pipelines (add features, split config, train model), link prediction pipelines.

How to Pass the Neo4j GDS Certification Exam

What You Need to Know

Passing score: 80%
Exam length: 50 questions
Time limit: 60 minutes
Exam fee: Free

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Neo4j GDS Certification Study Tips from Top Performers

1Memorize the four execution modes (stream, stats, mutate, write) and know when to use each in a workflow.

2Understand the difference between native projection and Cypher projection — use Cypher projection for derived relationships or computed properties.

3Practice CALL gds.* procedure syntax in Neo4j Sandbox until algorithm calls and configuration parameters feel natural.

4Know PageRank, Betweenness Centrality, Louvain, WCC, and Dijkstra deeply — these are the most-tested algorithms.

5Understand FastRP vs Node2Vec: FastRP is faster and linear, Node2Vec captures structural equivalence via biased random walks.

6Review ML pipeline steps: add node properties as features, configure train/test split, train a model, and evaluate performance.

Frequently Asked Questions

What is the Neo4j Graph Data Science library?

The Neo4j Graph Data Science (GDS) library is a plugin for Neo4j that provides graph algorithms and machine learning utilities. It works by projecting a subgraph into an in-memory graph, running algorithms on that projection, and returning or persisting results. It supports over 60 graph algorithms across centrality, community detection, pathfinding, similarity, and link prediction categories.

What are the four GDS algorithm execution modes?

stream returns results as rows to the client for immediate use. stats returns aggregate statistics without modifying the graph. mutate adds results as node/relationship properties to the in-memory projected graph for use in subsequent algorithm calls. write persists results back to the Neo4j database as permanent node or relationship properties.

When would you use PageRank versus Betweenness Centrality?

PageRank measures influence based on the quantity and quality of incoming relationships — good for finding influential nodes like authority pages or influential people. Betweenness Centrality measures how often a node lies on the shortest path between other nodes — good for finding bridges, brokers, or bottlenecks in a network.

What is the difference between Louvain and Label Propagation?

Louvain optimizes modularity hierarchically, producing nested community structures and a modularity score. It is deterministic per run but varies across runs. Label Propagation is a fast heuristic where nodes adopt the most common label among neighbors. It is faster than Louvain but less stable. Both detect communities in large graphs.

How do FastRP embeddings work?

FastRP (Fast Random Projection) generates node embeddings by iteratively aggregating neighborhood features with random projections. It is much faster than Node2Vec and scales to large graphs. You configure embeddingDimension (vector size), iterationWeights (neighborhood depth importance), and normalizationStrength. The resulting embedding vectors can feed downstream ML models.