HBase ensures data integrity during write operations through the Write-Ahead Log (WAL). Before making changes to the data store, HBase writes the modifications to the WAL. In the event of a failure, the system can recover the changes from the WAL, ensuring data consistency and durability.

The Columnar Storage feature of Sqoop would be most appropriate for extracting specific columns efficiently from a large database table. It optimizes the storage and retrieval of columnar data, enhancing performance for selective column extraction.

Rack Awareness is a critical consideration in Hadoop cluster capacity planning for ensuring high availability and fault tolerance. It involves the awareness of the physical location of nodes in racks, allowing Hadoop to replicate data across racks to enhance fault tolerance and reduce the risk of data loss.

Hadoop Streaming API's performance in processing real-time data can be improved by integrating Apache Kafka. Kafka provides high-throughput, fault-tolerant, and scalable messaging, making it a suitable choice for streaming data integration with Hadoop.

Flume agents are composed of sources, sinks, and channels, which are responsible for data flow. Sources collect data, channels store and transport the data between sources and sinks, and sinks deliver the data to the destination. Channels act as the conduit for the data flow within Flume.

The correct option is 'Bundle.' In Oozie, a Bundle is a key feature that allows the integration with systems outside of Hadoop for triggering workflows. It helps in managing and coordinating multiple workflows as a single unit, facilitating more complex data processing scenarios.

In a scenario of slow data processing, examining the configuration parameter related to MapReduce Slots is crucial. MapReduce slots determine the parallelism of data processing tasks, and adjusting this parameter can optimize the performance of the Hadoop cluster.

Avro is a commonly used file format in Hadoop for efficient large-scale data processing. Avro's compact binary format and schema evolution capabilities make it suitable for storing and exchanging data between Hadoop components. It is particularly useful in scenarios where flexibility and efficiency in handling complex data structures are essential.

Ticket Granting Ticket (TGT) is a mechanism in Hadoop security that allows users to obtain and renew tokens for accessing various cluster services. It plays a crucial role in the Kerberos authentication process.

The Hadoop cluster configuration file that must be appropriately set up for efficient data processing is core-site.xml. This file contains configurations for the Hadoop core components and settings such as I/O settings and default filesystem name.

In scenarios requiring large-scale data transformation, Apache Spark is often chosen for optimal performance. Spark's in-memory processing and efficient data processing engine make it suitable for handling complex transformations on large datasets with speed and scalability.

An 'FileNotFound' exception in Hadoop is often caused by an incorrect input path specified in the job configuration. It's essential to verify and provide the correct input path to ensure that the Hadoop job can locate and process the required data.