  In this paper, we present our approach to tackle Qur'an QA 2023 shared tasks
A and B. To address the challenge of low-resourced training data, we rely on
transfer learning together with a voting ensemble to improve prediction
stability across multiple runs. Additionally, we employ different architectures
and learning mechanisms for a range of Arabic pre-trained transformer-based
models for both tasks. To identify unanswerable questions, we propose using a
thresholding mechanism. Our top-performing systems greatly surpass the baseline
performance on the hidden split, achieving a MAP score of 25.05% for task A and
a partial Average Precision (pAP) of 57.11% for task B.
