This paper reports the results of the CultranAI system's participation in the PalmX cultural evaluation shared task, which focused on data augmentation and LoRA fine-tuning of a large-scale language model (LLM) for Arabic cultural knowledge representation. Leveraging the PalmX dataset and integrating it with the Palm dataset, we constructed a new dataset consisting of over 22,000 culturally based multiple-choice questions (MCQs). After benchmarking multiple LLMs to identify the optimal model, the Fanar-1-9B-Instruct model achieved the highest performance. This model was then fine-tuned on the combined augmented dataset of over 22,000 MCQs. The evaluation results showed an accuracy of 70.50% on the blind test set, ranking 5th, and an accuracy of 84.1% on the PalmX development set.