Apache Spark Certification Practice Test

The statement that GraphX only works on static graphs is inaccurate, making it the correct choice for the option that is not a characteristic of GraphX. In reality, GraphX is capable of handling both static and dynamic graphs, allowing modifications and updates to the graph structure during processing. This flexibility is vital for many real-world applications where data is constantly changing and requires real-time updates.

GraphX indeed provides a unified API that supports both graph processing and data-parallel computations, which facilitates the combination of graph algorithms with traditional data processing tasks. This capability enhances the versatility of graph analytics within Spark's ecosystem.

Moreover, GraphX supports iterative algorithms effectively, which is a central feature of many graph processing tasks. Iterative algorithms often require multiple passes over the graph data, and GraphX is optimized to handle such operations efficiently.

Lastly, GraphX provides an in-memory representation of graphs, which allows for high-performance processing and quick access to graph data. This feature is a crucial aspect of Spark's overall architecture, enabling faster computations compared to disk-based approaches.

Overall, the ability to work with both static and dynamic graphs ensures that GraphX remains a powerful tool for various graph processing needs, contradicting the notion that it is limited to static graphs only.