
Explanation:
Correct answer: B. Using a single-threaded write operation to Azure Cosmos DB. This approach does not enhance fault tolerance and data consistency because it relies on a single thread to write data to the sink, leading to potential bottlenecks and failures. Other options like enabling checkpointing, implementing idempotent writes, and employing write-ahead logs (WAL) are mechanisms that enhance fault tolerance and data consistency by ensuring recovery from failures, avoiding duplicate data, and maintaining data integrity.
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Which approach does not enhance fault tolerance and data consistency when using Apache Spark to process streaming data ingested from Azure IoT Hub?
A
Implementing idempotent writes to the sink
B
Using a single-threaded write operation to Azure Cosmos DB
C
Enabling checkpointing in Spark streaming
D
Employing write-ahead logs (WAL) for Spark streaming