True or False: Hadoop has a distributed architecture capable of handling vast amounts of data.

Hadoop is indeed built on a distributed architecture designed specifically to handle massive volumes of data efficiently. It employs a scalable storage and processing model that allows it to distribute data across multiple nodes in a cluster. This capability means that as data volume increases, additional nodes can be added to the cluster, enabling the system to scale horizontally.

The core components of the Hadoop ecosystem, namely Hadoop Distributed File System (HDFS) and MapReduce, work together to achieve this distributed processing. HDFS allows for the storage of large data sets across many machines, while MapReduce facilitates the processing of this data concurrently, leveraging the distributed nature of the architecture.

This design makes Hadoop an ideal choice for big data applications and analytics, as it can efficiently manage both structured and unstructured data while providing fault tolerance and data replication features.

The other options do not align with the fundamental characteristics of Hadoop. The false assertion in the other choices misrepresents Hadoop's capabilities and typical use cases, which are centered around handling large-scale data rather than being limited to smaller datasets or dependent on specific configurations.