Option B is the correct first step in troubleshooting a failed Spark job due to an OutOfMemoryError. Checking the logs will provide insights into the specific cause of the error, which could be due to various reasons such as memory leaks, inefficient data structures, or improper configuration. While options A and C could be part of the resolution strategy, they should be considered after diagnosing the issue. Option D is not advisable as it does not address the root cause of the failure.