Data Engineering Associate with Databricks Practice Exam 2025 – All-in-One Guide to Mastering Your Certification!

Spark ensures fault tolerance primarily through the use of Resilient Distributed Datasets (RDDs). RDDs are a core data structure in Spark that provide a robust way to manage distributed datasets. Each RDD is immutable and can be created from existing datasets through transformations. The fundamental feature that enables fault tolerance is the lineage information that RDDs maintain. This lineage allows Spark to reconstruct lost data due to node failures by re-computing the data from original sources instead of restoring it from backups. This computation-centric approach means that even if a partition of data is lost, it can be rebuilt based on the transformations applied to the original dataset.

In contrast, options such as backing up data to an external system do contribute to data recovery strategies but are not inherent to Spark's operation. High availability clusters refer to the infrastructure setup aimed at minimizing downtime but do not address the specifics of how Spark processes and handles data faults. Similarly, while data replication across multiple nodes can be part of a fault tolerance strategy in distributed systems, it is not the primary mechanism employed by Spark for achieving fault tolerance. Instead, the use of RDDs with their lineage tracking effectively allows Spark to maintain high availability and recover from failures seamlessly.