The Backlogged Input Events metric in Azure Stream Analytics indicates the number of input events that are queued and waiting to be processed by the job. A non-zero value (in this case, consistently at 20 for an hour) signifies that the job cannot process incoming events at the same rate they are arriving, leading to a backlog.

Why Option C (Increase the streaming units for the job) is correct:

• Streaming units (SUs) represent the compute resources allocated to a Stream Analytics job. Increasing SUs scales out the job, providing more processing capacity to handle higher event throughput.
• This directly addresses the root cause of backlog by enabling the job to process events faster, reducing the queue of waiting events.
• Microsoft's official documentation explicitly recommends scaling out (increasing SUs) when Backlogged Input Events are consistently non-zero or increasing.

Why other options are incorrect:

• Option A (Drop late arriving events): This would result in data loss and doesn't address the underlying capacity issue. It's not a recommended approach for handling backlogs.
• Option B (Add an Azure Storage account): While storage accounts are used for reference data or output sinks, adding one doesn't increase processing capacity or resolve backlog issues.
• Option D (Stop the job): This would completely halt processing, which doesn't reduce the backlog but rather stops all operations entirely.

The optimal solution is to scale the job appropriately by increasing streaming units to match the incoming event rate.