Using data augmentation techniques to generate additional training samples with variations in wording and phrasing can help the model better understand the nuances of user queries and improve its performance. Adding more random utterances (Option A) may not necessarily improve the model's understanding of the intents. Merging intents into a single intent (Option C) could result in loss of important information and reduce the model's accuracy. Fine-tuning the model's parameters (Option D) can be helpful, but it is not as effective as data augmentation in improving the model's ability to handle variations in user queries.