Parquet is the preferred file format for large-scale data processing in Hadoop due to its columnar storage, compression techniques, and schema evolution support. It offers high performance for analytical queries and is well-suited for data warehouse applications.

The implementation of a Combiner in a MapReduce job impacts performance by reducing the intermediate data volume. A Combiner combines the output of the Mapper phase locally on each node, reducing the data that needs to be transferred to the Reducer. This minimizes network traffic and improves overall job efficiency.

Apache Kafka handles high-throughput data streaming through the feature of topic partitioning. This allows Kafka to divide and parallelize the processing of data across multiple partitions, enabling scalability and efficient data streaming in Hadoop environments.

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.

Hive's LLAP (Live Long and Process) feature enhances query performance and enables the handling of large-scale data warehousing jobs by providing low-latency query responses.