The choice of file format, such as Parquet or ORC, impacts performance through the storage format. These formats optimize storage and retrieval, affecting factors like compression, columnar storage, and efficient data serialization. The right format can significantly enhance query performance in analytics workloads.

Apache Oozie integrates with other Hadoop ecosystem components, such as Hive and Pig, through Action Nodes. These nodes define specific tasks, such as MapReduce, Pig, or Hive jobs, and orchestrate their execution as part of the workflow.

The Fairness of a Hadoop cluster indicates the balance of load across its nodes. It ensures that each node receives a fair share of tasks, preventing resource imbalance and improving overall cluster efficiency.

The module in Apache Spark specifically designed for SQL and structured data processing is Spark SQL. It provides a programming interface for data manipulation using SQL queries, enabling users to seamlessly integrate SQL queries with Spark applications.

The correct option is 'SLA (Service Level Agreement).' In advanced Oozie workflows, SLA is used to manage job retries and error handling. It provides a mechanism to define and enforce performance expectations for various jobs within the workflow.

Apache Flume's architecture supports distributed data collection through an agent-based model. Agents are responsible for collecting, aggregating, and transporting data across the distributed environment. This approach enables flexibility and scalability in handling diverse data sources and destinations.

Data Replication is a key feature in Hadoop that ensures data processing continuity in the event of a DataNode failure. Hadoop replicates data across multiple nodes, and in case one node fails, the processing can seamlessly continue with a replicated copy from another node.

Data compression is crucial for managing and handling large datasets effectively in Hadoop development. Compression reduces the storage space required for data, speeds up data transmission, and enhances overall system performance by reducing the I/O load on the storage infrastructure.

Hadoop administrators manage data replication and distribution through block placement policies. These policies determine how Hadoop places and replicates data blocks across the cluster, optimizing for fault tolerance, performance, and data locality. Manual configurations, automatic balancing, and compression techniques are also essential aspects of data management in Hadoop.

When facing a sudden increase in data volume, horizontal scaling is recommended. This involves adding more nodes to the existing cluster, distributing the data processing load, and ensuring scalability by increasing the overall cluster capacity.