Apache Spark Certification 2025 – 400 Free Practice Questions to Pass the Exam

The correct answer highlights that Spark worker programs can run in a cluster and local threads. This approach is essential to how Spark is designed to handle distributed computing and parallel processing.

In a cluster environment, Spark can efficiently manage resources across multiple nodes, enabling it to perform large-scale data processing tasks. It utilizes the computational power of these nodes to distribute workloads, ensuring high performance and reduced execution times for data-heavy applications. Spark's architecture is built around the concept of resilience and scalability found in cluster setups, which allow it to handle fault-tolerance and task scheduling seamlessly.

On the other hand, running Spark worker programs in local threads allows for easier debugging and development. When running an application locally during development, Spark executes its tasks using threads in a single JVM, which simplifies testing without the overhead of a full cluster environment.

The other options do not accurately encapsulate the environments in which Spark workers operate. While local machines and clouds are environments where Spark can run, they don't reflect the intrinsic architectural separation of working with clusters and thread management that is fundamental to Spark's design. Virtual machines and laptops, as well as remote servers and desktops, generally refer to specific hardware rather than defining the operational capabilities of Spark workers in relation to distributed processing or parallel execution, which is